Search

Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation-plot records to the habitats of the EUNIS system, use it to classify a European vegetation-plot database, and compile statistically-derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS-ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set-theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species-to-habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man-made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS-ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment. The previous versions of the expert system and related reports were produced within a contract from the European Environment Agency to Wageningen Environmental Research and Masaryk University. The opinions expressed are those of the contractor and do not represent the Agency's official position. EVA data management and preparation of this paper were supported by the Czech Science Foundation (project no. 19-28491X to MC, LT, IK, TP, CM, JDa, MH, PN, DZ, GB, AJ, AKu, ZL and DV). IB and JAC were supported by the Basque Government (project no. T936-16). TB, ET, and LK were supported by the Ministry of Science and Higher Education of the Russian Federation (TB and ET project no. AAAA-A18-118052590019-7; LK project no. AAAA-A19-119012490096-2).

Aim: To disentangle the influence of environmental factors at different spatial grains (regional and local) on fern and lycophyte species richness and ask how regional and plot-level richness are related to each other. Location: Global. Time period: Present. Major Taxa studied: Ferns and lycophytes. Methods: We explored fern and lycophyte species richness at two spatial grains, regional (hexagonal grid cells of 7,666 km2) and plot level (300–500 m2), in relation to environmental data at regional and local grains (the 7,666 km2 hexagonal grid cells and 4 km2 square grid cells, respectively). For the regional grain, we obtained species richness data for 1,243 spatial units and used them together with climatic and topographical predictors to model global fern richness. For the plot-level grain, we collated a global dataset of nearly 83,000 vegetation plots with a surface area in the range 300–500 m2 in which all fern and lycophyte species had been counted. We used structural equation modelling to identify which regional and local factors have the biggest effect on plot-level fern and lycophyte species richness worldwide. We investigate how plot-level richness is related to modelled regional richness at the plot's location. Results: Plot-level fern and lycophyte species richness were best explained by models allowing a link between regional environment and plot-level richness. A link between regional richness and plot-level richness was essential, as models without it were rejected, while models without the regional environment-plot-level richness link were still valid but had a worse goodness-of-fit value. Plot-level richness showed a hump-shaped relationship with regional richness. Main conclusions: Regional environment and regional fern and lycophyte species richness each are important determinants of plot-level richness, and the inclusion of one does not substitute the inclusion of the other. Plot-level richness increases with regional richness until a saturation point is reached, after which plot-level richness decreases despite increasing regional richness, possibly reflecting species interactions.

International audience; We aimed to answer the question of whether the species richness and composition of calcareous grasslands in North-western Germany had changed over the last 70 years as a result of atmospheric nitrogen (N) deposition. In total, 1186 plots of Festuco-Brometea (alliance Bromion erecti) grasslands from the sub-oceanic regions of the country were compiled (1061 plots from literature sources spanning a time period from 1936 to 1996, 125 new plots from 2008). Environmental descriptors recorded for each plot included geographic coordinates, altitude, heat index (combining slope and aspect), mean Ellenberg indicator values for light, soil moisture, soil pH and soil N, and cumulative N deposition (the latter being highly positively correlated with the year of sampling). In a Detrended Correspondence Analysis, the sample plot scores along axis one were highly correlated with the mean Ellenberg N-values, those along axis two were significantly affected by the year of sampling. In a general linear model, species richness of vascular plants showed a markedly hump-shaped relationship with mean Ellenberg N-value, whereas it was weakly affected by year (cumulative N load). Species with a significant negative trend over time were more often (than expected by chance) habitat specialists of dry grasslands, small, light-demanding and winter-green or evergreen with smaller seeds and scleromorphic leaves. In contrast to what has been found for acidic grasslands, N deposition in calcareous grasslands did not result in a decline in species richness, most likely because calcareous grasslands are water- and phosphorus-limited, and are well-buffered in terms of soil pH. To prevent a further change in species composition towards more mesophytic communities, grassland management by the site managers needs to be intensified.