Understanding germination characteristics, including optimal stratification requirements of target species, is necessary for supporting grassland restoration yet poorly understood. This knowledge is essential for effective conservation, particularly with climate change altering germination conditions and thus germination capacity of plants. Here we studied the effect of three different durations of warm dry and cold wet stratification treatments, and their combinations in a full factorial design (in total 15 different pre-germination treatments), on the germination capacity of 48 grassland species native to Central Europe. Stratification treatments modelled present and forecasted summer (1-3 months warm period) and winter (1-3 months cold period) temperature conditions, while the study of the combined effect of these treatments is especially important in spring-germinating species. As response variables, we calculated relative response indexes and germination uncertainties of each species separately and applied general linear models to study the effect of treatments on these variables. We found clear effect of warm- or cold stratification on relative response indexes only in four species: strong positive response to warm stratification was found in Silene conica, while strong positive response to cold stratification was found in Agrimonia eupatoria, Echium vulgare, and Plantago lanceolata. The responses to treatment combinations were contradictory or lacked clear trends in most of the species. Germination uncertainty in general was high for all species, supporting the fact that Central European grassland species often rely on bet hedging as risk spreading strategy, to avoid unfavourable conditions during seedling establishment.

• Klíčová slova
• Climate change, Germination capacity, Grassland specialist species, Seed dormancy, Stratification,
• Publikační typ
• časopisecké články MeSH

Here, we use 7437 stable oxygen (δ18O) isotope ratios extracted from 192 living and relict Alpine trees to reconstruct trends and extremes in European summer hydroclimate from 8980 before the present to 2014 Common Era. Our continuous tree-ring δ18O record reveals a significant long-term drying trend over much of the Holocene (P < 0.001), which is in line with orbital forcing and independent evidence from proxy reconstructions and model simulations. Wetter conditions in the early-to-mid Holocene coincide with the African Humid Period, whereas the most severe summer droughts of the past 9000 years are found during the Little Ice Age in the 18th and 19th centuries Common Era. We suggest that much of Europe was not only warmer but also wetter during most of the preindustrial Holocene, which implies a close relationship between insolation changes and long-term hydroclimate trends that likely affected natural and societal systems across a wide range of spatiotemporal scales.

• Publikační typ
• časopisecké články MeSH

Drought is one of the main threats to food security and ecosystem productivity. During the past decades, Europe has experienced a series of droughts that caused substantial socioeconomic losses and environmental impacts. A key question is whether there are some similar characteristics in these droughts, especially when compared to the droughts that occurred further in the past. Answering this question is impossible with traditional single-index approaches and also short-term and often spatially inconsistent records. Here, using a multidimensional machine learning-based clustering algorithm and the hydrologic reconstruction of European drought, we determine the dominant drought types and investigate the changes in drought typology. We report a substantial increase in shorter warm-season droughts that are concurrent with an increase in potential evapotranspiration. If shifts reported here persist, then we will need new adaptive water management policies and, in the long run, we may observe considerable alterations in vegetation regimes and ecosystem functioning.

• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH