Biodiversity & Ecology

English

Abstract: This paper describes the vegetation database created as part of the Countryside Survey (CS) of Great Britain (GIVD ID EU-GB-003) which was established to monitor ecological and land use change in 1978 (http://www.countrysidesurvey.org.uk). The sample design is based on a series of stratified, randomly selected 1 km squares, which numbered 256 in the 1978 survey, 500 in the 1990 survey, 569 in the 1998 survey and 591 in the 2007 survey. Stratification of sample squares was based on predefined strata (called land classes) which have been derived from a classification of all 1 km squares in Britain based on their topographic, climatic and geological attributes obtained from published maps. A series of vegetation plots were located within each 1 km square using a restricted randomisation procedure designed to reduce aggregation. Linear features (road verges, watercourse banks, hedges, arable margins and field boundaries) and areal features (fields, unenclosed land and small semi-natural biotope patches) were sampled. Linear plots were 1 x 10 m laid out along a feature whilst unenclosed land and small biotopes were sampled using 2 m x 2 m plots. Larger randomly-placed plots were nested 14 m² plots with an inner nest of 2 m x 2 m. Within each 1 km Countryside Survey sample square the land cover and all landscape features were mapped and each parcel of land (and vegetation plot) has been assigned to a Broad Habitat/EUNIS habitat type. This database of vegetation plots is a very useful resource. The data is freely available from the website, however, there are restrictions on the release of the spatial location of the plots. There is now a considerable time-series of plots within the database going back to 1978 representing different habitat types and landscape features that can be analysed to determine changes in vegetation metrics (e.g. Ellenberg scores) and individual species. Vegetation changes can be linked to environmental drivers and the spatial scale (across GB) is sufficiently large to analyse gradients in most driving variables.