Hemiparasitic plants are considered as ecosystem engineers because they can modify the interactions between hosts and other organisms. Thereby, they may affect vegetation structure, community dynamics and facilitate coexistence as they are able to reduce interspecific competition by parasitizing selectively on competitive species and promote subordinate ones. In agri-environmental schemes, introducing the hemiparasite Rhinanthus has therefore been suggested as a low-cost method to increase grassland plant diversity, which is still subject to debate. 2 The majority of previous studies simply compared sites with and without hemiparasites. However, as hemiparasite effects are most likely density-dependent, we present a novel approach assessing the effect of Rhinanthus alectorolophus density on grassland plant diversity, yield and biomass quality. Moreover, we investigated whether functional plant composition and community mean traits are affected, which has been largely neglected in previous studies. The relationship between species richness and relative Rhinanthus biomass followed an optimum curve with highest values at 31% relative Rhinanthus biomass. At this Rhinanthusbiomass level, species richness was increased by 12% and yield decreased by 26% compared with plots without Rhinanthus. At relative Rhinanthus biomass > 60%, species richness was even lower than in plots without Rhinanthus. Overall, the biomass of grasses and the cumulative cover of legumes decreased linearly with increasing relative Rhinanthus biomass. Community mean trait analysis revealed that an increasing Rhinanthus density shifts the community composition towards smaller plant species. Biomass quality was not affected by increasing relative Rhinanthus biomass. In summary, our results of increased plant diversity − in line with a slightly lower yield but similar biomass quality − indicate that Rhinanthus is a suitable biological tool for grassland restoration.

BackgroundParasitoid wasps of the genus Pteromalus play an important role in biological pest control, however, the genus includes a large number of cryptic species, which makes reliable identification difficult. The latest identification key dates back to Graham (1969) and since then many new species have been described and nomenclatural changes proposed.New informationHere we present an interactive and fully illustrated identification key in Xper3 for 27 species of the Pteromalus albipennis species group as well as for 18 similar species. In addition to qualitative traits, a large set of body measurements is incorporated in the key. We also explored a new set of qualitative features on the propodeum and metasternum. During field work, a new species of the P. albipennis species group, P. capito Baur sp. n., could be reared from flower heads of Asteraceae, which is described here. It looks very similar to P. albipennis and P. cingulipes, however, several qualitative characters and body ratios distinguish it clearly from the most similar species.

Large-scale training sets enabling quantitative reconstructions of past fire parameters are needed to better assess potential effects of increased fire hazard under global warming conditions. The aim of this article is to validate recently developed continental regression equations for the reconstruction of fire number, intensity and size. These transfer functions were built by linking satellite data and charcoal collected in annually sampled sediment traps. We apply these European regression equations to four annually layered lakes located on a North–South gradient in Europe. Down-core annual microscopic charcoal (MIC) and macroscopic charcoal (MAC) influx values were compared with satellite-derived time series of fire number, fire intensity and area burned. Results show that the match between predicted and observed values improves when the overall mean and median of sampled years (12 and 9 years) are considered. Especially, the comparisons of median values show a very good agreement between charcoal-inferred and satellite-observed fire-regime parameters. MIC-based predictions underestimate the variability of the observed fire parameters and MAC-based predictions overestimate it. Our results imply that median values of the fire parameters can be reconstructed well by using MIC and MAC, while it is more difficult to infer the variability of fire-regime parameters. However, when MIC- and MAC-based predictions are pooled together, the fit between observed and predicted values increases for both medians and variability. This finding suggests that MIC and MAC are complementary proxies, thus best sedimentary fire reconstructions may be achieved when they are used together. We conclude that sediment traps can be used for the construction of continental-scale training sets and that their results can be applied to Holocene sedimentary charcoal sequences.