Mapping livestock grazing in semi-natural areas in the European Union and United Kingdom

Landscape Ecology(2024)

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摘要
Context Livestock grazing throughout Europe has resulted in high diversity of semi-natural areas in past centuries. Currently, most low intensity grazing relying on semi-natural vegetation is found primarily in marginal lands. These areas still host a high-level of biodiversity but are subject to abandonment and agricultural intensification. Objectives Spatial information on areas where semi-natural vegetation is still grazed, and how contextual geographic conditions encourage or limit grazing is missing, hindering their protection. We present an interdisciplinary approach to map the spatial distribution of grazing in semi-natural areas of the European Union (EU) and the United Kingdom (UK). Methods We first interviewed grazing experts from European countries, who provided us with estimates on grazing across selected land cover classes per environmental zone and Member State. Subsequently, we analysed the spatial distribution of grazing through maximum entropy modeling using pan-European in-situ data on grazing observations (using LUCAS, an EU wide land use survey) and a set of geographic characteristics representing the local socio-economic, terrain, soil and climatic context. Results The expert-derived estimates on grazing suggest that 20.6% (or 134 thousand km 2 ) of semi-natural areas in the EU + UK are grazed, although with low livestock densities. In addition, we find that there is great variety across the region in the factors that explain the occurrence of grazing: while in some regions, farmers’ age and distance to markets are most important, in others terrain or climate are influencing the location of grazing. Finally, we were able to map both the grazing probability as well as actual spatial distribution of grazing on semi-natural areas for the whole of EU and UK. Conclusions These data can assist in prioritizing future conservation efforts in these unique land systems.
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关键词
Semi-natural areas,Livestock grazing,Machine learning,Land use and land cover
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