Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism!

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 releves of vegetation composed mostly of common and generalist species, which are under-represented in historical data. Stratified random sampling seems to be a suitable toot 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.