Aim To produce a statistical stratification of the European environment, suitable for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent, and to provide strata for modelling exercises and reporting.

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Aim To develop a novel global spatial framework for the integration and analysis of ecological and environmental data. Location The global land surface excluding Antarctica. Methods A broad set of climate‐related variables were considered for inclusion in a quantitative model, which partitions geographic space into bioclimate regions. Statistical screening produced a subset of relevant bioclimate variables, which were further compacted into fewer independent dimensions using principal components analysis (PCA). An ISODATA clustering routine was then used to classify the principal components into relatively homogeneous environmental strata. The strata were aggregated into global environmental zones based on the attribute distances between strata to provide structure and support a consistent nomenclature. Results The global environmental stratification (GEnS) consists of 125 strata, which have been aggregated into 18 global environmental zones. The stratification has a 30 arcsec resolution (equivalent to 0.86 km2 at the equator). Aggregations of the strata were compared with nine existing global, continental and national bioclimate and ecosystem classifications using the Kappa statistic. Values range between 0.54 and 0.72, indicating good agreement in bioclimate and ecosystem patterns between existing maps and the GEnS. Main conclusions The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non‐commercial use through the GEO portal (http://www.geoportal.org).

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Summary1. Ellenberg's indicator values scale the¯ora of a region along gradients re¯ecting light, temperature, continentality, moisture, soil pH, fertility and salinity. They can be used to monitor environmental change. 2. Ellenberg values can be extended from central Europe, for which they were de®ned, to nearby parts of Europe. Given a database of quadrat samples, they can be repredicted by a simple algorithm consisting of two-way weighted averaging, followed by local regression. 3. A database of British samples was assembled from two large surveys. Ellenberg values were repredicted. 4. Except for the indicator of continentality, the correlation of repredicted and original values was in the range 0´72 (light) to 0´91 (moisture). The continentality indicator could not be adequately repredicted by the algorithm, and is unusable in Britain. Discrepancies between original and repredicted values can be attributed to various causes, including wrong original values, diering ecological requirements inBritain and central Europe, biased sampling of the British range of habitats, and the occurrence of small plants in shaded or basic microhabitats within well illuminated or predominantly acid quadrats. 6. The repredicted values were generally reliable, but a small proportion was clearly wrong. Wrong values were due to either inadequate sampling of species' realized niches in Britain or sampling with quadrats that were too large and included species that were not close associates.

The Institute of Terrestrial Ecology (ITE) has classified the 1 km squares in Great Britain (GB) into thirtytwo environmental strata, termed land classes, as a basis for ecological survey. The classes have been used in biogeographical studies of the distribution of individual species and species assemblages. The concept behind the technique is that there is an association between the environmental character of land and ecological parameters. The initial classification was based on a sample of squares drawn from a regular grid. The data for the 1212 1 km squares classified were drawn from published maps; the number of squares was limited by the available computing power. Subsequently the availability of more powerful computers and the need to improve geographical definition have led to the allocation of every 1 km square to its appropriate class. This paper has been written to summarise the principles involved in the development of the system and indicate the range of projects for which it has been used. The extension of the classification from a sample to the complete coverage of GB revealed the importance of the structure and style of data used to produce the classification. The significance of these conclusions for future work is discussed, with particular reference to automated methods of data capture.

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