Data from: A national-scale model of linear features improves predictions of farmland biodiversity

1. Modelling species distribution and abundance is important for many
conservation applications, but it is typically performed using relatively
coarse-scale environmental variables such as the area of broad land-cover
types. Fine-scale environmental data capturing the most
biologically-relevant variables have the potential to improve these
models. For example, field studies have demonstrated the importance of
linear features, such as hedgerows, for multiple taxa, but the absence of
large-scale datasets of their extent prevents their inclusion in
large-scale modelling studies. 2. We assessed whether a novel spatial
dataset mapping linear and woody linear features across the UK improves
the performance of abundance models of 18 bird and 24 butterfly species
across 3723 and 1547 UK monitoring sites respectively. 3. Although
improvements in explanatory power were small, the inclusion of linear
features data significantly improved model predictive performance for many
species. For some species, the importance of linear features depended on
landscape context, with greater importance in agricultural areas. 4.
Synthesis and applications. This study demonstrates that a national-scale
model of the extent and distribution of linear features improves
predictions of farmland biodiversity. The ability to model spatial
variability in the role of linear features will be important in targeting
agri-environment schemes to maximally deliver biodiversity benefits.
Although this study focuses on farmland, data on the extent of different
linear features are likely to improve species distribution and abundance
models in a wide range of systems, and also can potentially be used to
assess habitat connectivity. 10-Mar-2017