Agricultural land use and Skylark Alauda arvensis: a case study linking a habitat association model to spatially explicit change scenarios

The development of forward scenarios is a useful method of envisaging the environmental implications of potential changes in land use, as a tool for policy development. In this paper, a spatially explicit case study is used to provide insight into the environmental impacts of Common Agricultural Policy reform on Skylark Alauda arvensis, a species which is widespread on arable farmland, breeds in crops and has declined in recent decades. A generalized linear mixed model was used to estimate Skylark breeding population densities in different crops, using survey data collected from farms in the east of England, supplemented by the literature. Model outputs were then used to predict Skylark densities in an East Anglian Joint Character Area dominated by arable cropping. Predicted densities were mapped at field level using GIS, based on actual cropping derived from Integrated Administration and Control System data collected for the administration of subsidy payments. Three future scenarios were then created, based on expert opinion of potential changes in cropping over the next 5 years, and potential changes in Skylark density mapped on the basis of the predicted changes in cropping patterns. Overall, Skylark densities were predicted to decrease on average by 11–14% under ‘market-led’ (increasing wheat and oilseed rape, reduced set-aside) and ‘energy crop’ (5% area under short rotation coppice) scenarios, but remained virtually unchanged under an ‘environment-led’ (diverse cropping) scenario. The ‘market-led’ scenario is closest to short-term agricultural trajectories, but wider cultivation of biomass energy crops as modelled under the ‘energy crop’ scenario could occur in the medium term if energy policies are favourable. Appropriate mitigation strategies therefore need to be implemented if a continued decline in the Skylark population on lowland arable farmland is to be averted. The results provide a readily accessible visualization of the potential impacts of land-use change for policy-makers; similar techniques could be applied to visualize effects of changes arising through other drivers, including climate change.