Spatio-temporal modelling of the effect of selected environmental and land-use factors on species-rich calcareous grasslands: overgrazing and nitrogen deposition

The abundance of sensitive plant species in calcareous grasslands are threatened by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment. In order to quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of calcareous grasslands, large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model. The analyzed vegetation data come from 100 Danish sites monitored over an eight year period. The important sources of measurement- and sampling uncertainties have been included using a hierarchical model structure. Furthermore, the measurement- and sampling uncertainties are separated from the process uncertainty, which is important when generating ecological predictions that may feed into local conservation management decisions. There were significant negative effects of grazing and nitrogen deposition on the change in cover of sensitive plant species. Whereas the negative effect of nitrogen deposition on the cover of sensitive species was expected, it was surprising that the model results suggest that the class of sensitive species may be subjected to overgrazing by the grazing regimes that are currently applied at protected Danish calcareous grasslands. The standardized regression coefficients suggest that the effects of both grazing and nitrogen deposition are relatively benign compared to the effects of soil type, soil pH and precipitation. The relatively large effect of precipitation is interesting, since it is predicted that the amount of precipitation and distribution over the season will change due to climate change, and the results suggest that climate change may lead to important species compositional changes in calcareous grasslands. The fitted model may be used to quantify the uncertainties when generating ecological forecasting and local adaptive management plans.