• Energy Research

Open forests are among the world’s most species-rich ecosystems but these are now suffering from strong environmental pressure. With the increasing frequency of drought events accompanying climate change, open forests are now at high risk of canopy openness, an associated increase in nutrient load, and a consequent reordering of the plant community. Based on an 11-year field manipulative experiment, we examined whether the reintroduction of traditional forest uses, in particular the regular removal of litter which has been practiced for many centuries in Europe, may mitigate eutrophication and protect the ancient plant diversity of open forests. We examined vascular plant composition in 64, 3 × 3 m plots in 32 randomized blocks, each consisted of one litter-removal and one control plot in a subcontinental thermophilous oak forest dominated by Quercus petraea (Czech Republic, Central Europe). We analyzed how regular litter removal influenced the changes in topsoil nutrients and diversity and abundances of perennial, short-lived monocarpic and open oak forest plant species after eleven years of the annual application of this management. The regular litter removal significantly mitigated eutrophication by a reducing total nitrogen, and significantly decreased the amounts of base cations Ca2+ and Mg2+ in the topsoil. The studied plant species groups responded differently. Perennial and open oak forest species significantly increased their richness and abundances in the litter-removal plots and at the same time their diversity did not change significantly depending on inter-annual weather conditions. In contrast, litter removal negatively affected abundances of short-lived monocarpic species, whose richness was also considerably influenced by weather conditions (decreased the most in years with spring droughts). Moreover, we have found a protective effect of regular litter removal on drought-sensitive open oak forest species that survived in the litter-removal plots, whereas some completely disappeared from control sites during the period of repeated drought events. Our study thus demonstrates that litter removal is a tool with high potential in maintaining biodiversity in open forests affected by climate change.

AbstractThe stress‐gradient hypothesis predicts a switch from competition to facilitation, under increasing environmental stress. However, it is unclear how important is the change in competition–facilitation balance (i.e., the net outcome of plant–plant interactions) along the stress gradient in the regulation of community temporal stability (i.e., the inverse of temporal variability in total biomass). Increasing environmental stress may enhance community temporal stability by reduced competition or eventually by leading to facilitative interactions between the dominant and subordinate species. Here, we present the results of a 5‐yr mesocosm experiment that demonstrates the effects of interspecific interactions on the temporal stability of a riparian community across different drought‐stress scenarios. We constructed artificial communities of dominant species (Carex elata) and three subordinate species to simulate the independent effects of environmental stress and interspecific interactions. Using removal of the dominant species, we evaluated the interplay of various mechanisms regulating the temporal stability of the subordinate species (competition–facilitation balance, species asynchrony, and dominant species stability). By simultaneous testing of these stabilizing mechanisms, we show their importance differs depending on environmental variability and harshness. The predominant role is taken by species asynchrony in a seasonally dry environment, whereas in a permanently dry environment, the importance of reduced competition increases. Reduced competition was stabilizing, in particular through increased total community biomass, whereas species asynchrony increased total community biomass and decreased biomass variation. These results suggest experiments and simulations that exclude interspecific interactions may not offer realistic predictions of the effects of changing hydrological regimes on ecosystem functioning.

# Contrasting responses of forest phenological guilds to complex floodplain change [https://doi.org/10.5061/dryad.1vhhmgr2c](https://doi.org/10.5061/dryad.1vhhmgr2c) In total, we collected 28 explanatory variables that are assumed to have a significant effect on the abundance and richness of the herbaceous layer in floodplain forests and divided them into the categories of climatic, hydrological, and soil conditions, and forest canopy structure. Most of the factors were represented by the differences in their values between the current and historical samples (hereafter referred to as Δ). Changes in climatic conditions and hydrological regimes were calculated using the summary statistic (mean) of the year of sampling and the ten-year values (eleven years in total) of the variable before sampling to remove potential bias from a season of unusual conditions. Most variables therefore provide information on absolute changes in environmental conditions between these two periods. Differences were not calculated for most of the soil characteristics that were only measured in the current sampling period. Climatic and hydrological changes were calculated for spring (March-May), summer (June-August), autumn (September-November), and winter (December-February) in order to distinguish between different effects of seasonal changes on plant phenological guilds. Climatic variables included data on changes in temperature and the Standardised Precipitation-Evapotranspiration Index (SPEI). They were used to describe changes in the length of the growing season and the balance between drought and humidity. SPEI was calculated from monthly potential evapotranspiration (PE) using the Thornthwaite equation (Thornthwaite 1948). SPEI expresses the climatic water balance in each month as precipitation minus potential evapotranspiration (Vicente-Serrano et al. 2010, Beguería et al. 2014). The hydrological regime was represented by variables describing changes in the flood regime of the river. Flooding was characterised by the difference between the daily discharge (m^3/s) and the flood reached or exceeded once a year on the long-term average (i.e., *Q1* according to the Czech Hydrological Institute). We also calculated the relative elevation (m) of each sample plot above/below the flood level reached or exceeded once every five years, using a digital terrain model (DMR 4G, total standard error of elevation 0.3 m) and maps showing areas covered by 5-year floods. Soil conditions were represented by values of soil pH and plant bioavailable nutrients (Ca, K, Mg, P), which indicate the level of nutrient availability to plants (mg/kg). The proportions of soil particle-size fractions (*clay* < 0.001 mm, *clay* *and fine silt* < 0.01 mm, *coarse silt* 0.01–0.05 mm, *fine sand* 0.05–0.25 mm, *coarse sand* 0.25–2 mm) indicated sedimentation processes in river alluvium with increasing proportions of coarse sand particles, lower water and nutrient availability for plants. Soil acidity (pH-H2O) was the only soil characteristic measured during the historical data collection and its changes could indicate potential site acidification. Canopy structure was characterised by changes in canopy cover, expressed as the sum of the percent cover of all species present in the tree layer, and tree species diversity, expressed as species richness in the tree layer. Canopy cover of *Quercus robur* (%) represents the occurrence of key species in hard-wood riparian forests in Europe. Bray-Curtis dissimilarity was calculated using non-transformed species cover values between the tree species composition recorded during the current and historical sampling. Changes in tree litter quality were estimated using an ordinal scale (1-5) following Maes et al. (2019), with increasing values indicating higher litter quality. These variables characterise canopy succession and are thought to influence herbaceous plant development through changes in light and soil nutrient availability and microclimate. **References** Beguería, S., Vicente‐Serrano, S. M., Reig, F., & Latorre, B. (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. *International Journal of Climatology*, 34(10), 3001–3023. Maes, S. L., Blondeel, H., Perring, M. P., Depauw, L., Brūmelis, G., Brunet, J., Decocq, G., den Ouden, J., Härdtle, W., Hédl, R., Heinken, T., Heinrichs, S., Jaroszewicz, B., Kirby, K. J., Kopecký, M., Máliš, F., Wulf, M., & Verheyen, K. (2019). Litter quality, land-use history, and nitrogen deposition effects on topsoil conditions across European temperate deciduous forests. *Forest Ecology and Management*, 433, 405–418. Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. *Geographical Review*, 38(1), 55–94. Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. *Journal of Climate*, 23(7), 1696–1718. ## Description of the data and file structure Data contains a table with all the variables (in columns) needed to test the effects of environmental variables on temporal changes in species diversity and abundance of plant species in floodplain forests. Below is an explanation of each variable in the table. * *SampleID*: Identification number of the resurveyed plot * *Plant_phenological_guild*: Corresponds to month (i.e. 3-10 means March to October) when given plant phenological guild is flowering. * *Species_coverDIFF*: Difference between current and original total herbaceous plant cover in plot (%) * *Species_richnessDIFF*: Difference between current and original plant herbaceous species richness in plot * *Management*: Mature forest was the forest stand that had not been harvested since the historical sampling. Clear-cut forest had been cut and replanted (Mature or Clear-cut) * *YearDIFF*: The time between the original and current samples * *YearOriginal*: Year of original sampling * *YearCurrent*: Year of current re-sampling * *Latitude* and *Longitude*: WGS coordinates * *Soil_PS_001*: Proportion (%) of soil particle size *clay* < 0.001 mm * *Soil_PS_01*: Proportion (%) of soil particle size *clay* *and fine silt* < 0.01 mm * *Soil_PS_01_05*: Proportion (%) of soil particle size *coarse silt* 0.01–0.05 mm * *Soil_PS_05*: Proportion (%) of soil particle size *fine sand* 0.05–0.25 mm * *Soil_PS_05_25*: Proportion (%) of soil particle size *coarse sand* 0.25–2 mm * *pHDIFF*: Difference between current and original sampling in pH * *pH_Orig*: Soil reaction in original sampling * *Ca*: plant-available calcium (mg/kg) * *K*: plant-available potassium (mg/kg) * *Mg*: plant-available magnesium (mg/kg) * *P*: plant-available phosphorus (mg/kg) * *Relative_elevation*: The relative elevation (m) of each sample plot above/below the flood level reached or exceeded once every five years * *Tree_richnessDIFF*: Difference between current and original sampling in species richness in the tree layer * *QuercusDIFF*: Difference between current and original sampling in canopy cover of *Quercus robur* (%) * TreeCoverDIFF: Difference between current and original sampling in tree canopy cover (%), expressed as the sum of the percent cover of all species present in the tree layer * *Litter_CWMDIFF*: Difference between current and original sampling in tree litter quality estimated using an ordinal scale (1-5) and calculated as a community weighted mean (CWM) of all tree species present * *Tree_richness_Orig*: Species richness in the tree layer in the original sampling * *Quercus_Orig*: Canopy cover of *Quercus robur* (%) in original sampling * *TreeCover_Orig*: Tree canopy cover (%) in original sampling, expressed as the sum of the percent cover of all species present in the tree layer * *Litter_CWM_Orig*: Tree litter quality in original sampling was estimated using an ordinal scale (1-5) and calculated as a community-weighted mean (CWM) of all tree species present * *Species_cover_Orig*: Total herbaceous plant cover (%) in original sampling * *TreeDiss*: Bray-Curtis dissimilarity was calculated using non-transformed species cover values between the tree species composition recorded during the current and original sampling * *Species_richness_Orig*: Plant herbaceous species richness in original sampling * *Temp_WinterDIFF*: Difference between current and original sampling in mean winter temperature (December-February) * *Temp_SpringDIFF*: Difference between current and original sampling in mean spring temperature (March-May) * *Temp_SummerDIFF*: Difference between current and original sampling in mean summer temperature (June-August) * *Temp_AutumnDIFF*: Difference between current and original sampling in mean autumn temperature (September-November) * *Aridity_WinterDIFF*: Difference between current and original sampling in winter (December-February) Standardised Precipitation-Evapotranspiration Index (SPEI) * *Aridity_SpringDIFF*: Difference between current and original sampling in spring (March-May) Standardised Precipitation-Evapotranspiration Index (SPEI) * *Aridity_SummerDIFF*: Difference between current and original sampling in summer (June-August) Standardised Precipitation-Evapotranspiration Index (SPEI) * *Aridity_AutumnDIFF*: Difference between current and original sampling in autumn (September-November) Standardised Precipitation-Evapotranspiration Index (SPEI) * *Temp_Winter_Orig*: Mean winter temperature (December-February) in original sampling * *Temp_Spring_Orig*: Mean spring temperature (March-May) in original sampling * *Temp_Summer_Orig*: Mean summer temperature (June-August) in the original sampling * *Temp_Autumn_Orig*: Mean autumn temperature (September-November) in original sampling * *Aridity_Winter_Orig*: Winter (December-February) Standardised Precipitation-Evapotranspiration Index (SPEI) in original sampling * *Aridity_Spring_Orig*: Spring (March-May) Standardised Precipitation-Evapotranspiration Index (SPEI) in original sampling * *Aridity_Summer_Orig*: Summer (June-August) Standardised Precipitation-Evapotranspiration Index (SPEI) in original sampling * *Aridity_Autumn_Orig*: The autumn (September-November) Standardised Precipitation-Evapotranspiration Index (SPEI) in the original sampling * *Flooding_WinterDIFF*: Difference between current and original sampling in winter flooding (December-February), i.e. mean daily discharge (m3/s) minus value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute) * *Flooding_SpringDIFF*: Difference between current and original sampling in spring flooding (March-May), i.e. mean daily discharge (m3/s) minus value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute) * *Flooding_SummerDIFF*: Difference between current and original sampling in summer flooding (June-August), i.e. mean daily discharge (m3/s) minus value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute) * *Flooding_AutumnDIFF*: Difference between current and original sampling in autumn flooding (September-November), i.e. mean daily discharge (m3/s) minus value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute) * *Flooding_Winter_Orig*: Winter flooding (December-February), i.e. mean daily discharge (m3/s) minus the value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute). * *Flooding_Spring_Orig*: Spring flooding (March-May), i.e. mean daily discharge (m3/s) minus the value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute). * *Flooding_Summer_Orig*: Summer flooding (June-August), i.e. mean daily discharge (m3/s) minus the value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute). * *Flooding_Autumn_Orig*: Autumn flooding (September-November), i.e. mean daily discharge (m3/s) minus the value the flood reached or exceeded once a year on the long-term average (= *Q1* according to the Czech Hydrological Institute). ## Sharing/Access information NA ## Code/Software NA Understanding the seasonal dynamics of the forest herb layer and the factors that influence it is essential for predicting how the forest ecosystem, including mutualistic interactions, will respond to global change. However, to date, no research has investigated how understorey phenological guilds respond to major environmental threats and forest interventions. We showed the marked changes in the phenological structure of floodplain forest plant communities over half a century at a multi-regional scale in Central Europe. Unlike previous studies focusing on individual species' responses to climate change, we examined shifts in species richness, frequency, and abundance between phenological guilds within the plant community. We examined the effects of temporal variation in climate, hydrology, soil conditions, and canopy structure, and assessed the effects of no-intervention and intervention management on phenological guild responses. Our results show a significant loss of local species richness and a decline in species frequency in the forest understorey vegetation, associated with species exhibiting summer phenology. In contrast, an increase in abundance and richness of spring flowering species was observed, attributed to high nutrient loads and reduced flooding, respectively. The abundance of spring species increased only in stands where canopy cover increased over time, probably due to the suppression of summer flowering species, allowing spring flowering species to spread on open bare ground. The community experienced a shift in maximum flowering plant richness from June to May. Despite management interventions such as clear-cutting and tree planting, our data show similar temporal trajectories in intervention and non-intervention forests, indicating that changes in phenological structure are largely independent of recent management activities. Synthesis. We propose that a complex interplay of environmental factors, rather than climate change alone, is shaping shifts towards earlier phenological guilds in floodplain forests. This study highlights the importance of considering a comprehensive view of phenological guild dynamics and the impact of environmental factors on forest plant community structure in the face of global change.

AbstractGenetic admixture is supposed to be an important trigger of species expansions because it can create the potential for selection of genotypes suitable for new climatic conditions. Up until now, however, no continent‐wide population genetic study has performed a detailed reconstruction of admixture events during natural species expansions. To fill this gap, we analysed the postglacial history of Alnus glutinosa, a keystone species of European swamp habitats, across its entire distribution range using two molecular markers, cpDNA and nuclear microsatellites. CpDNA revealed multiple southern refugia located in the Iberian, Apennine, Balkan and Anatolian Peninsulas, Corsica and North Africa. Analysis of microsatellites variation revealed three main directions of postglacial expansion: (i) from the northern part of the Iberian Peninsula to Western and Central Europe and subsequently to the British Isles, (ii) from the Apennine Peninsula to the Alps and (iii) from the eastern part of the Balkan Peninsula to the Carpathians followed by expansion towards the Northern European plains. This challenges the classical paradigm that most European populations originated from refugial areas in the Carpathians. It has been shown that colonizing lineages have met several times and formed secondary contact zones with unexpectedly high population genetic diversity in Central Europe and Scandinavia. On the contrary, limited genetic admixture in southern refugial areas of A. glutinosa renders rear‐edge populations in the Mediterranean region more vulnerable to extinction due to climate change.