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Water deficit (drought stress) massively restricts plant growth and the yield of crops; reducing the deleterious effects of drought is therefore of high agricultural relevance. Drought triggers diverse cellular processes including the inhibition of photosynthesis, the accumulation of cell‐damaging reactive oxygen species and gene expression reprogramming, besides others. Transcription factors (TF) are central regulators of transcriptional reprogramming and expression of many TF genes is affected by drought, including members of the NAC family. Here, we identify the NAC factor JUNGBRUNNEN1 (JUB1) as a regulator of drought tolerance in tomato (Solanum lycopersicum). Expression of tomato JUB1 (SlJUB1) is enhanced by various abiotic stresses, including drought. Inhibiting SlJUB1 by virus‐induced gene silencing drastically lowers drought tolerance concomitant with an increase in ion leakage, an elevation of hydrogen peroxide (H2O2) levels and a decrease in the expression of various drought‐responsive genes. In contrast, overexpression of AtJUB1 from Arabidopsis thaliana increases drought tolerance in tomato, alongside with a higher relative leaf water content during drought and reduced H2O2 levels. AtJUB1 was previously shown to stimulate expression of DREB2A, a TF involved in drought responses, and of the DELLA genes GAI and RGL1. We show here that SlJUB1 similarly controls the expression of the tomato orthologs SlDREB1, SlDREB2 and SlDELLA. Furthermore, AtJUB1 directly binds to the promoters of SlDREB1, SlDREB2 and SlDELLA in tomato. Our study highlights JUB1 as a transcriptional regulator of drought tolerance and suggests considerable conservation of the abiotic stress‐related gene regulatory networks controlled by this NAC factor between Arabidopsis and tomato.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Strong events of long-range transported biomass burning aerosol were detected during July 2013 at three EARLINET (European Aerosol Research Lidar Network) stations, namely Granada (Spain), Leipzig (Germany) and Warsaw (Poland). Satellite observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instruments, as well as modeling tools such as HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) and NAAPS (Navy Aerosol Analysis and Prediction System), have been used to estimate the sources and transport paths of those North American forest fire smoke particles. A multiwavelength Raman lidar technique was applied to obtain vertically resolved particle optical properties, and further inversion of those properties with a regularization algorithm allowed for retrieving microphysical information on the studied particles. The results highlight the presence of smoke layers of 1-2 km thickness, located at about 5 km a.s.l. altitude over Granada and Leipzig and around 2.5 km a.s.l. at Warsaw. These layers were intense, as they accounted for more than 30% of the total AOD (aerosol optical depth) in all cases, and presented optical and microphysical features typical for different aging degrees: color ratio of lidar ratios (LR532/LR355) around 2, alpha-related angstrom exponents of less than 1, effective radii of 0.3 mu m and large values of single scattering albedos (SSA), nearly spectrally independent. The intensive microphysical properties were compared with columnar retrievals form co-located AERONET (Aerosol Robotic Network) stations. The intensity of the layers was also characterized in terms of particle volume concentration, and then an experimental relationship between this magnitude and the particle extinction coefficient was established.
In spite of the fundamental role of the landscape water balance for the Earth's water and energy cycles, monitoring the water balance and its components beyond the point scale is notoriously difficult due to the multitude of flow and storage processes and their spatial heterogeneity. Here, we present the first field deployment of an iGrav superconducting gravimeter (SG) in a minimized enclosure for long-term integrative monitoring of water storage changes. Results of the field SG on a grassland site under wet-temperate climate conditions were compared to data provided by a nearby SG located in the controlled environment of an observatory building. The field system proves to provide gravity time series that are similarly precise as those of the observatory SG. At the same time, the field SG is more sensitive to hydrological variations than the observatory SG. We demonstrate that the gravity variations observed by the field setup are almost independent of the depth below the terrain surface where water storage changes occur (contrary to SGs in buildings), and thus the field SG system directly observes the total water storage change, i.e., the water balance, in its surroundings in an integrative way. We provide a framework to single out the water balance components actual evapotranspiration and lateral subsurface discharge from the gravity time series on annual to daily timescales. With about 99 and 85% of the gravity signal due to local water storage changes originating within a radius of 4000 and 200m around the instrument, respectively, this setup paves the road towards gravimetry as a continuous hydrological field-monitoring technique at the landscape scale.
Landscape and scale-dependent spatial niches of bats foraging above intensively used arable fields
(2017)
Introduction
Bats are threatened by agricultural intensification, and although bat ecology in agricultural landscapes is in the focus of current research, the effects of interacting spatiotemporal factors on species-specific bat activity above farmland remain understudied. Our aim was to identify spatiotemporal factors and their interactions relevant for the activity of bat species above conventionally managed arable fields.
Methods
We repeatedly monitored relative bat activity above open arable fields in Germany using acoustic monitoring. We used site-related biotic and abiotic factors and landscape characteristics across five spatial scales, their combinations, and interactions to identify those factors which best explain variation in bat activity.
Results
Numerous interactions between landscape characteristics and the insect abundance affected bat activity above fields. For instance, Pipistrellus pipistrellus became more active with increasing insect abundance, but only above fields with a low proportion of woody vegetation cover in the surroundings. Additionally, the level of bat activity in summer depended on landscape characteristics. For example, the activity of Pipistrellus nathusii was relatively low in summer above fields that were surrounded by vegetation patches with a high degree of edge complexity (e.g., hedgerow). However, the activity remained at a relatively high level and did not differ between seasons above fields that were surrounded by vegetation patches with a low degree of edge complexity (e.g., roundly shaped forest patch).
Conclusions
Our results revealed that landscape characteristics and their interactions with insect abundance affected bat activity above conventionally managed fields and highlighted the opportunistic foraging behavior of bats. To improve the conditions for bats in agricultural landscapes, we recommend re-establishing landscape heterogeneity to protect aquatic habitats and to increase arthropod availability.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
BACKGROUND: Reduced left ventricular ejection fraction (LVEF) ≤30% is the most powerful prognostic indicator for sudden cardiac death (SCD) in patients after myocardial infarction (MI), but there are little data about long-term changes of LVEF after revascularization and the following implantation of a cardioverter defibrillator (ICD).
METHODS: We performed a retrospective analysis of 277 patients with reduced LVEF at least 1month after MI and complete revascularization. Patients (median time post-MI 23.4months; 74.3% after PCI, 25.7% after CABG were assigned either to group 1 (LVEF<30%) or group 2 (LVEF 30-40%). Biplane echocardiography was redone after a mean follow-up of 441±220days.
RESULTS: LVEF increased significantly in both two groups (group 1: 26.2±4.8% to 32.4±8.5%; p<0.001; group 2: 38.2±2.5% to 44.4±9.6%; p<0.001). However, statistical analysis of first and second LVEF measurement by means of a LOWESS regression and with an appropriate correction of the regression towards the mean effect revealed only a moderate increase of the mean LVEF from 35 to 37% (p<0.001) with a large interindividual variation.
CONCLUSIONS: The impact of early revascularization on LVEF appears to be low in the majority of post-MI heart failure patients. Owing to the high variability, a single measurement may not be reliable enough to justify a decision on ICD indication.
Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.
In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.
The significance of biogenic silicon (BSi) pools as a key factor for the control of Si fluxes from terrestrial to aquatic ecosystems has been recognized for decades. However, while most research has been focused on phytogenic Si pools, knowledge of other BSi pools is still limited. We hypothesized that different BSi pools influence short-term changes in the water-soluble Si fraction in soils to different extents. To test our hypothesis we took plant (Calamagrostis epigejos, Phragmites australis) and soil samples in an artificial catchment in a post-mining landscape in the state of Brandenburg, Germany. We quantified phytogenic (phytoliths), protistic (diatom frustules and testate amoeba shells) and zoogenic (sponge spicules) Si pools as well as Tironextractable and water-soluble Si fractions in soils at the beginning (t(0)) and after 10 years (t(10)) of ecosystem development. As expected the results of Tiron extraction showed that there are no consistent changes in the amorphous Si pool at Chicken Creek (Huhnerwasser) as early as after 10 years. In contrast to t(0) we found increased water-soluble Si and BSi pools at t(10); thus we concluded that BSi pools are the main driver of short-term changes in water-soluble Si. However, because total BSi represents only small proportions of water-soluble Si at t(0) (< 2 %) and t(10) (2.8-4.3 %) we further concluded that smaller (< 5 mu m) and/or fragile phytogenic Si structures have the biggest impact on short-term changes in water-soluble Si. In this context, extracted phytoliths (> 5 mu m) only amounted to about 16% of total Si con-tents of plant materials of C. epigejos and P. australis at t(10); thus about 84% of small-scale and/or fragile phytogenic Si is not quantified by the used phytolith extraction method. Analyses of small-scale and fragile phytogenic Si structures are urgently needed in future work as they seem to represent the biggest and most reactive Si pool in soils. Thus they are the most important drivers of Si cycling in terrestrial biogeosystems.