Abstract

Aim This study aims to assess the sensitivity of calcareous grassland vegetation to climate change and to indicate the most probable direction of change. Location The study area was a region of Britain, Ireland, France and Spain, centred on the Bay of Biscay, which was defined using a land classification based on climatic criteria. Methods Vegetation was sampled in the field, with additional data collected on soils, climate, management and land cover. The vegetation samples were ordinated by detrended correspondence analysis in order to explore the main gradients present and as a basis for modelling changes. Environmental data were summarized by ordination techniques, with the scores generated used to predict the current vegetation score on the first two ordination axes by multiple regression. The model was then manipulated to represent a 2 °C increase in temperature and resulting shifts in the vegetation samples in terms of their species composition assessed. Results There was a good general agreement between the original vegetation ordination axis scores and those predicted by the model, the latter of which were based on environmental data alone. Following a 2 °C increase in temperature, the predicted changes in the ordination space were demonstrated to be subtle, consisting of small shifts towards vegetation associated with warmer conditions, representing distances 100 km or less on the ground. Main conclusions The models are simple but nevertheless provide a useful basis for the investigation of potential vegetation change. The shifts in the ordination space represent more minor changes than those predicted in previous studies. This suggests that the potential for major change is lower when environmental factors such as soil and management are considered in addition to climate. The potential for change is also reduced when vegetation is considered as a whole rather than on an individual species basis, due to both interspecific interactions and interactions with environmental factors acting as constraints.

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