Functionality of wet grasslands as green infrastructure

Habitat loss is a global issue that affects land cover patterns, ecological processes and the distribution and abundance of species. As a result, many conservation approaches have appeared, such as the European Union’s green infrastructure (GI) policy and UNESCO’s Biosphere Reserve (BR) concept. Both are being applied in southern Sweden’s Kristianstad Vattenrike Biosphere Reserve (KVBR). Despite concentrated conservation efforts at a local scale to conserve biodiversity, focal grasslands and waders have declined. This calls for the assessment of outputs within the KVBR in terms of both knowledge production and its dissemination, as well as the consequences of management on the ground (Paper I). Focusing on supporting the KVBRs’ work and wader conservation in general, this thesis studied how anthropogenic factors affect the land cover patterns and processes of wet grasslands for waders. Over the past two centuries land use and land cover change have reduced the KVBR’s area of functional grassland habitat by >98% (Paper II). Whilst loss and degradation of wet grassland habitats is considered a primary reason of wader decline in Europe, predator–prey relationships have been proposed as a secondary reason. Using several wet grassland landscapes across Northern Europe, predator-prey relationships were explored (Paper III, IV & V). Firstly, the distribution and abundance of avian predators is determined by resource diversity and anthropogenic factors of a landscape at multiple spatial scales. Secondly, predator abundance and predation pressure were positively correlated, and linked to different wet grassland developmental stages in Northern Europe. Thus, based on the studies contained in this thesis, changes to both land cover patterns and ecological processes play a vital role for the maintenance of wet grasslands as functional GI. Finally, the multiple landscape case study approach employed in this thesis is a novel macroecological tool that encourages knowledge production and learning for functional GI