| Summary: | Countryside recreation is hugely popular and demand is on the rise. Whilst participation should be encouraged, sensitive management is required to reduce associated environmental impacts. This thesis investigates current and future patterns in countryside recreation at multiple spatial scales, from national to site, to explore the potential impacts on biodiversity and enhance the evidence base for conservation interventions. A national-level recreation model is developed from a unique and massive data set of georeferenced recreational visits collected over 3 years, which predicts the probability of visitation as a function of land cover composition and accessibility to and within a site, whilst controlling for source population and socio-demographic differences. Land cover types were subdivided into proportion designated and non-designated for high nature value, using Sites of Special Scientific Interest (SSSI) as a proxy. Probability of visitation to preferred land covers, coast and freshwater, decreased when SSSI designated, with no effect for broadleaved woodland. Therefore general recreational use by the public did not represent an important ecosystem service of protected high-nature-value areas. The model was employed to create national- and county-level spatially-explicit predictions of countryside recreation under present and future conditions, the conservation implications of which are discussed. As species conservation requires knowledge of how recreational pressure is distributed throughout a site, a novel methodology was developed using Thetford Forest as a case study. GIS-based Network Analysis was combined with statistical modelling to predict the number of disturbance events from recreationists for all path sections throughout the site. This tool was able to test the consequences of altering site access on the number of hypothetical new woodlark territories likely to become occupied. This study contributes to a relatively small body of work on the importance of biodiversity for recreation and provides novel spatial approaches for quantifying demand and testing conservation interventions.
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