Abstract
Most of the historical phytosociological data on vegetation composition have been sampled preferentially and thus belong to those ecological data that do not fulfill the statistical assumption of independence of observations, necessary for valid statistical testing and inference. Nevertheless, phytosociological data have been recently used for various ecological meta-analyses, especially in studies of large-scale vegetation patterns. For this reason, we focus on the comparison of preferential sampling with other sampling designs that have been recommended as more convenient alternatives from the point of view of statistical theory. We discuss that while simple random sampling, systematic sampling and stratified random sampling better meet some of the statistical assumptions, preferential sampling yields data sets that cover a broader range of vegetation variability. Moreover, today’s large phytosociological databases provide huge amounts of vegetation data with unrivalled geographic extent and density. We conclude that in the near future ecologists will not be able to replace the preferentially sampled phytosociological data in large-scale studies. At the same time, phytosociological databases have to be complemented with relevés of vegetation composed mostly of common and generalist species, which are under-represented in historical data. Stratified random sampling seems to be a suitable tool for doing this. Nevertheless, a methodology and input data for stratification have to be developed to make stratified random sampling an ecologically more relevant and practical method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Austin M.P. &Heyligers P.C. (1989): Vegetation survey design for conservation: gradsect sampling of forests in Northeast New South Wales.Biol. Conservation 50: 13–32.
Blackburn T.M. &Gaston K.J. (2003):Macroecology. Concepts and consequences. Cambridge University Press, Cambridge.
Botta-Dukát Z., Chytrý M., Hájková P. &Havlová M. (2005): Vegetation of lowland wet meadows along a climatic continentality gradient in Central Europe.Preslia 77: 89–111.
Braun-Blanquet J. (1964):Pflanzensoziologie. Grundzüge der Vegetationskunde. Ed. 3. Springer, Wien.
Breiman L. (2001): Statistical modelling: The two cultures.Statist. Sci. 16: 199–231.
Breiman L., Friedman J.H., Olshen R.A. &Stone C.G. (1984):Classification and regression trees. Wadsworth International Group, Belmont.
Brown J.H. (1999): Macroecology: progress and prospect.Oikos 87: 3–14.
Bunce R.G.H., Barr C.J., Clarke R.T., Howard D.C. &Lane A.M.J. (1996): Land classification for strategic ecological survey.J. Environm. Managem. 47: 37–60.
Chytrý M. (2001): Phytosociological data give biased estimates of species richness.J. Veg. Sci. 12: 439–444.
Chytrý M., Pyšek P., Tichý L., Knollová I. &Danihelka J. (2005): Invasions by alien plants in the Czech Republic: a quantitative assessment across habitats.Preslia 77: 339–354.
Chytrý M. &Rafajová M. (2003): Czech National Phytosociological Database: basic statistics of the available vegetation-plot data.Preslia 75: 1–15.
Chytrý M., Tichý L. &Roleček J. (2003): Local and regional patterns of species richness in Central European vegetation types along the pH/calcium gradient.Folia Geobot. 38: 429–442.
Cochran W.G. (1977):Sampling techniques. Ed. 3. Wiley, New York.
Ewald J. (2003): A critique for phytosociology.J. Veg. Sci. 14: 291–296.
Fanelli G., Bianco M.P., Cazzagon P., D’Angeli D., De Corso S., De Sanctis M., Gioia P., Ramello A., Rinieri G., Serafini Sauli A., Tescarollo P., Testi A. &Pignatti S. (2005): Remote sensing in phytosociology: The map of vegetation of the Provincia of Rome.Ann. Bot. (Rome), Nuova Ser. 5: 171–181.
Goedickemeier I., Wildi O. &Kienast F. (1997): Sampling for vegetation survey: some properties of a GIS-based stratification compared to other statistical sampling methods.Coenoses 12: 43–50.
Grabherr G., Reiter K. &Willner W. (2003): Towards objectivity in vegetation classification: the example of the Austrian forests.Pl. Ecol. 169: 21–34.
Haila Y. &Margules C.R. (1996): Survey research in conservation biology.Ecography 19: 323–331.
Hájek M. &Hájková P. (2004): Environmental determinants of variation in CzechCalthion wet meadows: a synthesis of phytosociological data.Phytocoenologia 34: 33–54.
Hennekens S.M. &Schaminée J.H.J. (2001): TURBOVEG, a comprehensive data base management system for vegetation data.J. Veg. Sci. 12: 589–591.
Hirzel A. &Guisan A. (2002): Which is the optimal sampling strategy for habitat suitability modelling.Ecol. Modelling 157: 331–341.
Holeksa J. &Wożniak G. (2005): Biased vegetation patterns and detection of vegetation changes using phytosociological databases. A case study in the forests of the Babia Góra National Park (the West Carpathians, Poland).Phytocoenologia 35: 1–18.
Kenkel N.C., Juhász-Nagy P. &Podani J. (1989): On sampling procedures in population and community ecology.Vegetatio 83: 195–207.
Kent M. &Coker P. (1992):Vegetation description and analysis. A practical approach. CRC Press, Boca Raton.
Knollová I., Chytrý M., Tichý L. &Hájek O. (2005): Stratified resampling of phytosociological databases: some strategies for obtaining more representative data sets for classification studies.J. Veg. Sci. 16: 479–486.
Koenig W.D. (1999): Spatial autocorrelation of ecological phenomena.Trends Ecol. Evol. 14: 22–26.
Lájer K. (2007): Statistical tests as inappropriate tools for data analysis performed on non-random samples of plant communities.Folia Geobot. 42: 115–122.
Legendre P. (1993): Spatial autocorrelation: Trouble or new paradigm?Ecology 74: 1659–1673.
Legendre P., Dale M.R.T., Fortin M.J., Gurevitch J., Hohn M. &Myers D. (2002): The consequences of spatial structure for the design and analysis of ecological field surveys.Ecography 25: 601–615.
Legendre P. &Legendre L. (1998):Numerical ecology. Ed. 2. Elsevier, Amsterdam.
Lososová Z., Chytrý M., Cimalová Š., Otýpková Z., Pyšek P. &Tichý L. (2006): Classification of weed vegetation of arable land in the Czech Republic and Slovakia.Folia Geobot. 41: 259–273.
Lvončík S. (2006):Lesní vegetace Slezské pahorkatiny a Ostravské pánve (Forest vegetation of the Slezká Uplands and Ostrava Basin). Diploma thesis, Masaryk University, Brno.
Margules C.R. &Austin M.P. (1994): Biological models for monitoring species decline: the construction and use of data bases.Philos. Trans. R. Soc. London B 344: 69–75.
Mohler C.L. (1983): Effect of sampling pattern on estimation of species distributions along gradients.Vegetation 54: 97–102.
Moore J.J., Fitzsimons P., Lambe E. &White J. (1970): A comparison and evaluation of some phytosociological techniques.Vegetatio 20: 1–20.
Økland R.H. (2007): Wise use of statistical tools in ecological field studies.Folia Geobot. 42: 123–140.
Podani J. (1984): Spatial processes in the analysis of vegetation. Theory and review.Acta Bot. Hung. 30: 75–118.
Popper K.R. (1990):The logic of scientific discovery. Ed. 14. Unwin Hyman, London.
Pyšek P., Jarošík V., Chytrý M., Kropáč Z., Tichý L. &Wild J. (2005): Alien plants in temperate weed communities: prehistoric and recent invaders occupy different habitats.Ecology 86: 772–785.
Ricotta C. (2007): Random sampling does not exclude spatial dependence: the importance of neutral models for ecological hypothesis testing.Folia Geobot. 42: 153–160.
Ripley B.D. (1996):Pattern recognition and neural networks. Cambridge University Press, Cambridge.
Schmidtlein S. &Sassin J. (2004): Mapping of continuous floristic gradients in grasslands using hyperspectral imagery.Remote Sensing Environm. 92: 126–138.
Smart S.M., Clarke R.T., van de Poll H.M., Robertson E.J., Shield E.R., Bunce R.G.H. &Maskell L.C. (2003): National-scale vegetation change across Britain; an analysis of sample-based surveillance data from the Countryside Surveys of 1990 and 1998.J. Environm. Managem. 67: 239–254.
Stohlgren T.J., Coughenour M.B., Chong G.W., Binkley D., Kalkhan M.A., Schell L.D., Buckley D.J. &Berry J.K. (1997): Landscape analysis of plant diversity.Landscape Ecol. 12: 155–170.
ter Braak C.J.F. &Šmilauer P. (2002):CANOCO reference manual and CanoDraw for Windows user’s guide. Software for Canonical Community Ordination (version 4.5). Biometris, Wageningen & České Budějovice.
Vapnik V. (1998):Statistical learning theory. Wiley, New York.
Wessels K.J., van Jaarsveld A.S., Grimbeek J.D. &van der Linde M.J. (1998): An evaluation of the gradsect biological survey method.Biodivers. & Conservation 7: 1093–1121.
Westhoff V. &van der Maarel E. (1978): The Braun-Blanquet approach. In:Whittaker R.H. (ed.),Classification of plant communities, W. Junk, The Hague, pp. 287–399.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Roleček, J., Chytrý, M., Hájek, M. et al. Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism!. Folia Geobot 42, 199–208 (2007). https://doi.org/10.1007/BF02893886
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF02893886