Aim  In contrast to non‐forest vegetation, the species richness–productivity (SR‐P) relationship in forests still remains insufficiently explored. Several studies have focused on the diversity of the tree layer, but the species richness of temperate deciduous forests is mainly determined by their species‐rich herb layer. The factors controlling herb‐layer productivity may differ from those affecting tree layers or open herbaceous vegetation, and thus the SR‐P relationship and its underlying processes may differ. However, the few relevant studies have reported controversial results. Here we explore the SR‐P relationship in the forest herb layer across different areas from oceanic to continental Europe, and put the effect of habitat productivity on species richness into context with other key factors, namely soil pH and light availability. Location  North‐western Germany, Czech Republic, Slovakia and southern Urals (Russia). Methods  We measured herb‐layer species richness and biomass, soil pH and tree‐layer cover in 156 vegetation plots of 100 m2 in deciduous forests. We analysed the SR‐P relationship and the relative importance of environmental variables using regression models for particular areas and separate forest types. Results  We found a consistent monotonic increase in the herb‐layer species richness with productivity across all study areas and all forest types. Soil pH and light availability also affected species richness, but their relative importance differed among areas. Main conclusions  We suggest that the monotonically increasing SR‐P relationship in the forest herb layer results from the fact that herb‐layer productivity is limited by canopy shading; competition within the herb layer is therefore not strong enough to exclude many species. This differs fundamentally from open herbaceous vegetation, which is not subject to such productivity limits and consequently exhibits a unimodal SR‐P relationship. We present a conceptual model that might explain the differences in the SR‐P relationship between the forest herb layer and open herbaceous 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.

bstract Questions: Does plant species richness and composition of eastern Mediterranean dwarf shrubland (phrygana) correlate with soil pH? How important is the effect of pH on species diversity in relation to other environmental factors in this ecosystem? What is the evolutionary background of the diversity-pH relationship? Location: Western Crete, Greece. Methods: Species composition of vascular plants, soil and other environmental variables were sampled in 100-m 2 plots on acidic and basic bedrock in phrygana vegetation. The relationships between species composition and environmental variables (including climate) were tested using canonical correspondence analysis, and relationships between species richness and environment using correlation and regression analyses. Data were analysed separately for different plant functional types based on life form and life span. Results: Although soil pH varied across a narrow range (5.9-8.1), species composition changed significantly along the pH gradient within all plant functional types. For most functional types, the effect of soil pH on species composition was stronger than that of other environmental variables. Species richness of annuals, geophytes and suffruticose chamaephytes increased with soil pH, while richness of hemicryptophytes and shrubs was not correlated with pH. Conclusions: The results are consistent with the evolutionary species pool hypothesis. High numbers of calcicole annuals, geophytes and suffruticose chamaephytes may be a result of the evolution of these groups on base-rich dry soils in the Mediterranean climate. In contrast, hemicryptophytes, a life form typical of the temperate zone, evolved on both acidic and basic soils and therefore their species numbers do not respond to soil pH across the narrow range studied. The lack of a relationship between shrub species richness and pH is difficult to explain: it may reflect the more diverse or older origin of Mediterranean woody species and their conservative niches.

Questions It is generally acknowledged that the date of sampling partly determines the floristic composition and layer covers of vegetation plots. However, the effect of vegetation seasonality on the results of vegetation analyses is still insufficiently explored. Here, we investigated two data sets to examine how intra‐seasonal variability of vegetation influences the pattern of: (1) species composition; (2) species richness, number of unrecorded species, plant life‐form spectra; and (3) quantitative changes in cover of individual species and layers. Location Deciduous forests and dry grasslands in SE Czech Republic. Methods We established 40 forest and 46 dry grassland permanent plots, each sampled in three periods (spring, summer and autumn). We compared species composition and β‐diversity patterns in multi‐dimensional space between different periods of the growing season using permutational multivariate analyses (PERMANOVA, PERMDISP). We used ANOVA for repeated measures and Tukey post‐hoc tests to assess differences in species richness, proportions of unrecorded species, changes in layer cover and plant life‐form spectra. Results In the Forest data set, spring plots significantly differed in their species composition from summer and autumn plots, while we found significant differences between different periods of the growing season in the Dry grasslands data set. We confirmed, that the forest species richness was highest in summer plots, while the α‐diversity in spring and summer dry grassland plots were comparable and notably declined in autumn. Although most plant life forms were best recorded in summer, both forest geophytes and dry grassland therophytes were best recorded in spring. Conclusions We suggest that before analysing vegetation plot data, large databases should be carefully stratified according to date of sampling. Based on our results from temperate broad‐leaved forests and dry grasslands, we recommend excluding plots sampled before June, as they might be a potential source of misinterpretation of species composition analyses (with the exception of studies focused on vernal species). The use of autumn plots does not have any strong effect on the results, except in α‐diversity analyses in forest plots, while dry grassland plots are significantly different from the rest of the growing season.

No abstract