Variation in Leaf Morphological Traits of European Beech and Norway Spruce Over Two Decades in Switzerland

Leaf morphological traits (LMTs) of forest trees have been observed to vary across space and species. However, long-term records of LMTs are scarce, due to a lack of measurements and systematic leaf archives. This leaves a large gap in our understanding of the temporal dynamics and drivers of LMT variations, which may help us understand tree acclimation strategies. In our study, we used long-term LMT measurements from foliar material collections of European beech (Fagus sylvatica) and Norway spruce (Picea abies), performed every second year from 1995 to 2019 on the same trees within the Swiss Long-term Forest Ecosystem Research Program LWF. The 11 study plots (6 beech, 4 spruce, and 1 mixed) are distributed along gradients of elevation (485–1,650 m a.s.l.), mean annual precipitation (935–2142 mm), and mean annual temperature (3.2–9.8°C). The investigated LMTs were (i) leaf or needle mass, (ii) leaf area or needle length, and (iii) leaf mass per area or needle mass per length. We combined this unique data set with plot variables and long-term data on potential temporal drivers of LMT variations, including meteorological and tree trait data. We used univariate linear regressions and linear mixed-effects models to identify the main spatial and temporal drivers of LMT variations, respectively. For beech LMTs, our temporal analysis revealed effects of mast year and crown defoliation, and legacy effects of vapor pressure deficit and temperature in summer and autumn of the preceding year, but no clear long-term trend was observed. In contrast, spruce LMTs were mainly driven by current-year spring conditions, and only needle mass per length showed a decreasing long-term trend over the study period. In temporal models, we observed that LMTs of both species were influenced by elevation and foliar nutrient concentrations, and this finding was partly confirmed by our spatial analyses. Our results demonstrate the importance of temporal analysis for determining less recognized drivers and legacy effects that influence LMTs, which are difficult to determine across space and species. The observed differences in the temporal drivers of beech and spruce LMTs suggest differences in the adaptation and acclimation potential of the two species.

Trends and periodicities in sub-divisional rainfall

The monthly and annual rainfall data for 35 meteorological sub-divisions for the 87-year period (1901-1987) have been used to study the trends and periodicities of monsoon and annual rainfall series. A number of distribution-free statistical tests have been applied to the rainfall series for testing non-randomness. Comparison of the decadewise means with the mean of the whole period showed that, for the country as a whole, the annual rainfall indicated four different climatic periods -two periods of above normal rainfall from 1960-1965 and from 1975 onwards and two periods of below normal rainfall from 1901-1915 and 1965-1975 whereas the monsoon rainfall showed two different climatic periods-a period of below normal rainfall from 1901-1920 and a period of above normal rainfall from 1920 onwards. The series were also subjected to low-passfilters which showed the presence of significant long term trend for a few sub-divisions. The power spectrum analysis for the annual and monthly rainfall series for a large number of sub-divisions showed significant periodicities of 2. 1-3.6 years, which correspond to the frequency range of the QBO. In addition, periodicities of 5.1 to 10.0 years and 19.3 years or more were also significant for a number of sub-divisions.

Massive crop expansion threatens agriculture and water sustainability in Northwestern China

Northwestern China (NWC) is among the major global hotspots undergoing massive terrestrial water storage (TWS) depletion. Yet driver(s) underlying such region-wide depletion remain controversial, i.e., warming-induced glacier-melting versus anthropogenic activities. Reconciling this controversy is the core initial step to guide policy-making for combating the dual challenges in agriculture production and water scarcity in the vastly dry NWC towards sustainable development. Utilizing diverse observations, we found persistent cropland expansion by >1.2×104km2 since 2003, leading to 59.9% growth in irrigated area and 19.5% in agricultural water use, despite a steady irrigation efficiency enhancement. Correspondingly, a substantially faster evapotranspiration increase occurred in crop expansion areas, whereas precipitation exhibited no long-term trend. Counterfactual analyses suggest that the region-wide TWS depletion is unlikely to have occurred without crop expansion-driven evapotranspiration increase even in the presence of glacier-melting. These findings imply that sustainable water management is critically needed to ensure agriculture and water security in NWC.

PROSPECTS FOR INTERNATIONAL SCIENTIFIC AND SCIENTIFIC-TECHNICAL COOPERATION IN THE CONTEXT OF THE NEW TECHNOLOGICAL REALITY

The research is aimed at finding ways to fill the regulatory vacuum in which digital technologies develop.The article provides an assessment of the positive and negative impact of digitalization on public relations,highlights the problems associated with the legal regulation of public relations complicated by the use ofdigital technologies or the participation of artificial intelligence, analyzes the degree of knowledge of theproblem in legal science and the proposed models of legal regulation of digitalization. The authors haveconcluded that the development of digital technologies demonstrates a long-term trend towards a decreasein the protective abilities of existing legal institutions, and therefore, a public request is formed for theisolation in the system of international scientific law of a set of legal norms regulating scientific and technicalcooperation in the digitalization of science and study of informatization processes.

Prediction model of seasonality in the construction industry based on the accidentality phenomenon

The construction industry is an economic sector that is characterized by seasonality. Seasonal factors affect the volume of production, which in turn affects the accident rate. The aim of the research presented in the article was to develop a model for predicting the number of people injured in occupational accidents in the construction industry. Based on the analysis of statistical data and previous studies, the occurrence of certain regularities of the accidentality phenomenon was found, namely the long-term trend over many years, as well as seasonality and cyclicality over the course of a year. The found regularities were the basis for the assumptions that were made for the construction of the model. A mathematical model was built in the non-linear regression dimension. The model was validated by comparing the results of prediction errors generated by the developed model with the results of prediction errors generated by other known models, such as ARIMA, SARIMA, linear and polynomial models, which take into account the seasonality of the phenomenon. The constructed model enables the number of people injured in accidents in the construction industry in selected months of future years to be predicted with high accuracy. The obtained results can be the basis for making appropriate decisions regarding preventive and prophylactic measures in the construction industry. Commonly known mathematical tools available in the STATISTICA package were used to solve the given task.

Sunspots, GDP, and the Stock Market

A correlation has been observed between the US GDP and the number of sunspots as well as between the Dow Jones Industrial Average and the number of sunspots. The data cover 80 years of history. The observed correlations permit forecasts for the GDP and for the stock market in America with a future horizon of 10 years. Both being above their long-term trend they are forecasted to go over a peak around Jun-2008.