Aims: Eurasian forest-steppes are among the most complex non-tropical terrestrial ecosystems. Despite their considerable scientific, ecological and economic importance, knowledge of forest-steppes is limited, particularly at the continental scale.Here we provide an overview of Eurasian forest-steppes across the entire zone: (a) we propose an up-to-date definition of forest-steppes, (b) give a short physiogeographic outline, (c) delineate and briefly characterize the main forest-steppe regions, (d) explore forest-steppe biodiversity and conservation status, and (e) outline foreststeppe prospects under predicted climate change. Location: Eurasia (29°-56°N, 16°-139°E). Results and Conclusions: Forest-steppes are natural or near-natural vegetation complexes of arboreal and herbaceous components (typically distributed in a mosaic pattern) in the temperate zone, where the co-existence of forest and grassland is enabled primarily by the semi-humid to semi-arid climate, complemented by complex interactions of biotic and abiotic factors operating at multiple scales. This new definition includes lowland forest-grassland macromosaics (e.g. in Eastern Europe), exposurerelated mountain forest-steppes (e.g. in Inner Asia), fine-scale forest-grassland This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Aims Understanding fine‐grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine‐grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi‐natural) grasslands and natural grasslands are the richest vegetation type. The open‐access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high‐quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation‐plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.

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BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research.

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The traditional, low-input use of grassland in Central and Eastern Europe has provided high-quality food, clothing and manure for millennia. As an outcome of sustainable low-intensity agriculture, some rural areas have globally significant species richness. Traditional farming is still well preserved in several regions of the Carpathian Mountains. This is a unique opportunity to use the wisdom of our ancestors to keep grassland biodiversity for our descendants. We present a sampling methodology to survey traditionally managed grassland ecosystems holistically, including abiotic, biological and cultural phenomena, and reflect thus the multidimensionality of traditional farming. Our main objective was to reveal the connection between particular management practices and precisely measured plot plant diversity. Our motivation was to identify traditional farming approaches that result in both high biodiversity and sustainable grassland utilization in particular region, and confirm their impact also using statistical tests. The multitaxon vegetation sampling at seven spatial scales combined with soil analyses, detailed land-use information derived from interviews with the land parcel owners, satellite pictures and historical materials provide potentially valuable data for several scientific disciplines including syntaxonomy, plant ecology, environmental anthropology and ethnology. Examples of grassland management practices based on traditional ecological knowledge can serve as an inspiration for developing modern biodiversity conservation strategies applicable for rural regions. The database Grassland with Tradition is registered in Global Index of Vegetation-Plot Databases (GIVD) with the identifier ID EU-00-032. To date it contains data from 31 study sites in 7 countries (Austria, Czech Republic, Slovakia, Hungary, Poland, Romania, Ukraine). Syntaxonomic reference: Mucina et al. (2016).

How should the somewhat vague term of restoration success be measured? This is a critical question rooted in European law, where in fact the creation of proper replacement habitats is a prerequisite for permitting projects that trigger a loss of species or habitats. Previous studies have used indices that relied on a comparison to reference sites, for example the number of a predefined pool of target species or compositional similarity. However, since restoration sites have rarely the same biotic and abiotic conditions as reference sites, plant communities in restored sites will not perfectly match the reference sites. Furthermore, such indices fail when reference sites are lacking or degraded. Hence, there is a need for an alternative approach that evaluates the conservation value of a restored site independently from reference sites. We propose that naturalness indicator values can be an option to measure restoration success. The approach of using naturalness indicator values makes use of the fact that plants are able to indicate environmental parameters, including degradation and regeneration. We compared and measured the restoration success of three well-established methods for grassland restoration (sod transplantation, hay transfer, seeding) with three commonly used indices (diversity, number of target species, similarity to reference sites). The results verified earlier studies and showed that sod transplantation led to the highest restoration success followed by hay transfer and seeding of sitespecific seed mixtures. Further, we used those well-established indices for an evaluation of novel, naturalness-based indices (unweighted and cover-weighted mean naturalness indicator values, the sum of naturalness indicator values). While calculating the means of naturalness indicator values failed to offer conclusive information on restoration success, we could show that the sum of naturalness indicator values was highly correlated with the number of target species and compositional similarity to reference sites. Thus, our case study demonstrated that naturalness indices can be an excellent option to estimate success in grassland restoration. Abbreviations: CWMNN-Cover-weighted mean Naturalness indicator values, DSH-Donor site for hay transfer, DSS-Donor site for sod transplantation, FPFI-Frequency positive fidelity index (Tichý 2005), Simpson-Simpson´s Index, SUMNN-Sum of Naturalness indicator values, TGSpN-Number of target species, UWMNN-Unweighted Mean Naturalness indicator values. Naturalness indicators of grassland restoration 185 similar habitat conditions as compared to available reference sites and (b) are rather close to the reference sites (White and Walker 1997, Valkó et al. 2017).

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