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ambientale, sapienza università di roma, roma, italy; f Dipartimento di scienze e tecnologie Biologiche ed ambientali, università del salento, lecce, italy; g istituto Beni culturali, regione emilia-romagna, Bologna, italy; h centro conservazione Biodiversità (ccB), Dipartimento di scienze della Vita e dell'ambiente (DisVa), università di cagliari, cagliari, italy; i scuola di Bioscienze e medicina Veterinaria, università di camerino, macerata, italy; j Dipartimento di scienze della Vita, università di modena e reggio emilia, modena, italy; k Dipartimento di scienze della terra, dell'ambiente e della Vita (DistaV), università di genova, genova, italy; l Dipartimento di Biologia, ecologia, e scienze della terra (DiBest), università della calabria, cosenza, italy; m Dipartimento di scienze della Vita e Biologia dei sistemi (DBios), università di torino, torino, italy; n comitato scientifico, museo regionale di scienze naturali efisio noussan, aosta, italy; o sezione di Botanica filippo Parlatore, museo di storia naturale, università di firenze, firenze, italy; p Dipartimento di Biologia, università di napoli federico ii, napoli, italy; q Dipartimento di scienze agrarie, alimentari e forestali, università di Palermo, Palermo, italy; r scuola di scienze agrarie, forestali, alimentari ed ambientali, università della Basilicata, Potenza, italy; s strada Val san martino superiore, torino, italy; t centro ricerche floristiche marche, Pesaro, italy; u Dipartimento di Pianificazione, Design, tecnologia dell'architettura (PDta), sapienza università di roma, roma, italy; v Department of Botany, national museum of natural history, smithsonian institution, washington, Dc, usa; w Via isonzo, massa, italy; x Dipartimento di scienze della terra, università di torino, torino, italy; y Via regazzoni Bassa, Padova, italy; z museo di storia naturale della calabria ed orto Botanico, università della calabria, cosenza, italy; aa Dipartimento di scienze della Vita, università di trieste, trieste, italy; ab fondazione museo civico di rovereto, trento, italy; ac sezione di Botanica ed ecologia Vegetale, Dipartimento di scienze e tecnologie Biologiche, chimiche e farmaceutiche (steBicef), università di Palermo, Palermo, italy; ad Dipartimento di scienze agrarie e forestali (Dafne), università della tuscia, Viterbo, italy; ae Via europa unita, schio, italy; af istituto per le Piante da legno e l'ambiente (iPla), torino, italy; ag laboratori di Botanica, Dipartimento di scienze delle Produzioni agroalimentari e dell'ambiente, università di firenze, firenze, italy; ah largo Brigata cagliari, Vercelli, italy; ai Dipartimento di scienze e tecnologie ambientali, Biologiche e farmaceutiche, università della campania luigi Vanvitelli, caserta, italy;

Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm2 to 1,024 m2. Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.

Bioclimatology deals with the interrelation between climate and living organisms, in particular, plants and plant communities, considering the main climate variables that are relevant for species distribution. In this context spatial interpolation of monthly temperature and precipitation data using 203 rain gauges and 68 temperature gauges for Sardinia (Italy) was undertaken. As interpolation technique, we used regression kriging which combines multiple linear regression (MLR) with ordinary kriging of the residuals. MLR procedures include as independent variables: altitude, latitude, longitude, coast distance and a topographic factor of relative elevation. Elevation data were obtained from digital elevation model at 40 m resolution. Following the approach of the Worldwide Bioclimatic Classification System, a bioclimatic diagnosis of the entire territory was derived using map algebra calculations of the bioclimatic indices proposed by Rivas-Martínez et al. [(2011). Worldwide Bioclimatic classification system. Global Geobotany, 1, 1 -638]. Two macrobioclimates (Mediterranean pluviseasonal oceanic and Temperate oceanic), one macrobioclimatic variant (Submediterranean), and four classes of continentality (from weak semihyperoceanic to weak semicontinental), eight thermotypic horizons (from lower thermomediterranean to upper supratemperate) and seven ombrotypic horizons (from lower dry to lower hyperhumid) were identified, resulting in a combination of 43 isobioclimates. The resulting map represents a useful environmental stratum, for regional planning, ecological modeling and biodiversity conservation.

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