Fungi represent a group of soil microorganisms fulfilling important ecological functions. Although several studies have shown that yeasts represent a significant proportion of fungal communities, our current knowledge is based mainly on cultivation experiments. In this study, we used amplicon sequencing of environmental DNA to describe the composition of yeast communities in European temperate forest and to identify the potential biotic and abiotic drivers of community assembly. Based on the analysis of ITS2 PCR amplicons, yeasts represented a substantial proportion of fungal communities ranging from 0.4 to 14.3% of fungal sequences in soil and 0.2 to 9.9% in litter. The species richness at individual sites was 28 ± 9 in soil and 31 ± 11 in litter. The basidiomycetous yeasts dominated over ascomycetous ones. In litter, yeast communities differed significantly among beech-, oak- and spruce-dominated stands. Drivers of community assembly are probably more complex in soils and comprise the effects of environmental conditions and vegetation.

Questions Biomass is an important ecological property, but its measurement is destructive and time‐consuming and therefore generally missing for historical vegetation plots. Here we propose and test indirect estimation of herbaceous biomass using models based on easily obtainable variables, namely plant height and cover. We compare these models with Ellenberg indicator values for nutrients (EIVs Nutrients), which are sometimes used as an alternative measure of productivity. Location Czech Republic, western Slovakia. Methods Above‐ground biomass (dry weight; g·m−2) was regressed against the following explanatory variables: (1) Cover E1, total percentage cover of the herb layer visually estimated in the field; (2) Biomass estimate‐raw, ‐adjusted and ‐median, calculated from plant covers and heights (according to a local flora); and (3) mean EIVs Nutrients calculated per plot. For the analyses, we used four data sets containing a total of 469 plots from different vegetation types: ‘Wet meadows’, ‘Dry grasslands’, ‘Fen–dry grassland transects’ and ‘Forest herb layer’. To test the applicability of different biomass estimates we chose an example of a species richness–productivity relationship in the ‘Wet meadows’ data set and describe differences in resulting patterns. Results Both cover of herb layer and calculated ‘biomass volumes’ were more accurate in predicting biomass dry weight than EIVs Nutrients. The best results were obtained from the Biomass estimate‐median model that combines median stand height and total cover of the herb layer. Cover E1 showed relatively tight correlations with biomass, particularly in sparse vegetation, but was a rather poor predictor when cover values were high. This was especially noticeable in application of the Cover E1 model in analysis of the species richness–productivity relationship. Conclusions In contrast to biomass, cover of the herb layer has a fixed upper limit (100%), which may lead to misinterpretations in dense, structurally diverse vegetation. Most promising is the Biomass estimate‐median method, which can be applied both to already sampled plots by calculating median height from average species heights according to local floras and to newly sampled plots using the median of plant heights measured in the field. Therefore, we propose it as a rapid, non‐destructive alternative to biomass harvest.

Aim Soil pH is considered an important driver of fine‐scale plant species richness in terrestrial ecosystems. However, it is unclear to what extent this relationship is influenced by precipitation, which often directly affects both soil pH and species richness. We asked: (1) what is the relationship between fine‐scale vascular plant species richness and soil pH in regions with different levels of precipitation and (2) what are the relative effects of soil pH and precipitation on species richness? Location Dry grasslands in eight regions of northern Eurasia. Methods Species richness and soil pH were measured in 1055 10 m × 10 m plots and precipitation values were derived from global datasets. Relationships between variables were explored using general linear models, mixed‐effect models and partial regressions. Variation partitioning was used to assess the relative effect of each predictor on species richness. Results In wetter regions, soil pH range was broader, mean species richness was higher and the richness–pH relationship was unimodal. In drier regions, mean soil pH was higher and its range narrower, species richness was on average lower and less variable, and the richness–pH relationship was negative or absent. The richness–pH relationship persisted after controlling for the effect of precipitation, but precipitation, uniquely or together with soil pH, explained more variation in species richness in most regions than did pH alone. Main conclusions The relationship between plant species richness and soil pH in dry grasslands changes from unimodal, through negative, to none with decreasing regional precipitation in Eurasia. However, it seems that the species richness–soil pH relationship in dry grasslands over broad areas is substantially influenced and confounded by precipitation either indirectly, by shortening and shifting the pH gradient, or directly, by decreasing the negative effects of drought stress on richness.

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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