Abstract
Semi-natural grasslands have an important conservation value in Europe but have been in continuous decline since the early twentieth century. Valuable and well-preserved semi-natural grasslands must be identified in order to plan conservation and restoration strategies. The main aim of this paper is to develop an expert system based on diagnostic species for the identification and assessment of EU semi-natural grasslands using a dataset of 3238 vegetation plots from the Iberian Peninsula (Portugal and Spain, including the French Pyrenees). The dataset was obtained by stratified resampling of a larger dataset of 12,204 vegetation plots containing other related grasslands and herbaceous vegetation types. We applied a k-means classification on the resampled dataset and selected the diagnostic species for each group of EU semi-natural grasslands in order to develop an expert classification system with definitions based on their species composition and biogeographic distribution. The expert-based classification was applied to 14 vegetation subtypes belonging to semi-natural grasslands in the Habitats Directive, corresponding to semi-dry calcareous grasslands (Habitat 6210), Nardus grasslands (6230), hay meadows (6510, 6520) and Molinia meadows on peaty soils (6410). We provide a practical and easy tool for the identification, monitoring and assessment of EU semi-natural grasslands on the Iberian Peninsula.
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Communicated by Daniel Sanchez Mata.
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Taxonomic reference: Euro+Med PlantBase (ww2.bgbm.org/EuroPlusMed, accessed 9 February 2018). Syntaxonomic reference: Rivas-Martínez (2011).
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Rodríguez-Rojo, M.P., Font, X., García-Mijangos, I. et al. An expert system as an applied tool for the conservation of semi-natural grasslands on the Iberian Peninsula. Biodivers Conserv 29, 1977–1992 (2020). https://doi.org/10.1007/s10531-020-01963-1
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DOI: https://doi.org/10.1007/s10531-020-01963-1
Keywords
- Formalised classification
- Grasslands
- Habitats Directive
- Habitat classification
- Diagnostic species
- Vegetation database

