نبذة مختصرة : A long-term history of multi-functional management and natural disturbances have transformed many woodlands of Europe into unique landscapes that integrate forests, open grazed pastures with scattered shrubs and trees, in addition to various semi-natural vegetation elements. These landscapes, named wood-pastures, often represent economically valuable and biodiversity-rich habitats with a characteristic semi-open and heterogeneous vegetation structure and can be found throughout different European bioregions. In the Western Mediterranean the most important types of wood-pastures have a tree cover dominated by oaks and are known as “montados” (in Portugal) and dehesas (in Spain). Wood-pastures are increasingly fragile due to conflicting land-use regimes associated with intensification and abandonment, which influence vegetation structure and overall habitat heterogeneity leading to impacts on biodiversity and ecosystem processes. This situation highlights the urgent need to improve low-cost and biodiversity-friendly management strategies that have minimal negative effects on production to preserve the balance between natural and economic values of wood-pastures. The main objective of this thesis, which focuses on Mediterranean woodpastures, is to explore the biodiversity patterns of multiple taxa using the advantages of functional diversity approaches to reveal how land-use driven changes influence biodiversity, ecosystem functioning and ecosystem services in wood-pastures, and use these insights to improve sciencebased, sustainable management plans. For these aims, this thesis evaluated the role of small ungrazed patches within the wood-pasture matrix in enhancing (1) taxonomic and (2) functional diversity of multi-taxa (plants, beetles, lichens). The results revealed the contribution of allowing shrub growth within ungrazed patches to species richness and functional diversity of plants, beetles and lichens by facilitating the presence of distinct species and trait assemblages compared to woodpasture matrix. This ...
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