The ecological and economic relevance of sweet chestnut (Castanea sativa Mill.) has long been related to its widespread geographical distribution and multipurpose product potential. In Central Italy, chestnut management represents a paradigmatic example of the potential conflict between landowner targets and biodiversity conservation: options for preserving stand-scale biodiversity are not fully considered as current management is based on monospecific, even-aged coppice stands and clearcutting on wide areas. Relationships between silvicultural treatment and floristic diversity of chestnut coppices are here investigated focusing the attention on rotation length and on the role of thinning. Seven coppice stands were selected in such a way to be of similar size (about 10 ha) and to cover a wide range of ages and a different number of thinnings. Plot sampling was performed across the stands and their floristic diversity was compared and analyzed by means of indicators in order to assess statistical relationships between floristic data and stand structural attributes. The achieved results suggest alternative suitable options for managing chestnut coppice stands in order to enhance biodiversity while maintaining wood production.

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Secondary dry grasslands in Europe can host high levels of vascular plant richness at small spatial scales. However, in Southern Europe their biodiversity patterns are largely unexplored. In this work, we aim at: i) estimating plant species richness patterns at very fine scales in montane dry grasslands, on limestone bedrock, in Abruzzo Lazio & Molise National Park (Central Apennines, Italy); ii) assessing the most important physical and edaphic drivers of biodiversity patterns at multiple plot sizes. We used randomly placed nested-plot series where we measured alpha-diversity at three different plot sizes (1 m 2 , 0.1 m 2 and 0.01 m 2) and within-plot beta-diversity (as expressed by the slope of the species-area curve across plot sizes). Variable selection was performed by means of Random Forests. Relationships between selected variables and diversity measures were then assessed using Regression Trees, Linear and Generalized Linear Models. Overall, results pointed to topographically-controlled edaphic factors (soil pH and silt fraction) as the main drivers positively influencing alpha-diversity at all spatial scales, with a positive effect of rock cover and slope inclination at smaller spatial grains. Beta-diversity was positively influenced by rock cover. We suggest that high-pH, steep and/or rocky sites feature higher species richness because they lack A c c e p t e d m a n u s c r i p t 2 competitive grass species. Our results are in agreement with previous works underlining the importance of less productive habitats for the conservation of secondary grassland biodiversity.

& Key message The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping. & Context Forest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists. & Aims This work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature. & Methods A rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests.