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Исследование ландшафтов всегда было традиционным научным направлением географии. В России подобная направленность исследований остаётся актуальной, несмотря на то, что термины «геоэкология» и «ландшафтная экология» сегодня более распространены в англоязычном научном сообществе. Наш краткий обзор показывает значительное ускорение антропогенных ландшафтных изменений в Европе, Центральной Азии и азиатской части России за последние пять десятилетий. Ландшафтные исследования в антропоцене должны быть направлены на достижение и сохранение устойчивости ландшафта при его высокой производительности, что включает в себя прекращение деградации ландшафтов, развитие культурных и сохранение природных ландшафтов. Чистая вода и чистый воздух, плодородные и здоровые почвы для производства продуктов питания и других экосистемных услуг, а также биологически разнообразная зеленая среда являются атрибутами ландшафтов, обеспечивающих выживание и благополучие населения. Дисциплинарные и междисциплинарные исследования должны генерировать знания, инновации и правила принятия действенных решений. Генерация знаний в глобализованном мире основана на сборе больших массивов данных и моделировании сценариев. Международные длительные полевые опыты и системы агроэкологического мониторинга будут предоставлять данные для экосистемных моделей и систем поддержки принимаемых решений. Landscape research has been a traditional scientific discipline of geography. This is still the case in Russia, whilst the terms geo-ecology and landscape ecology have become established in the English speaking scientific community. Our short review reveals huge and accelerating anthropogenic landscape transformations in Europe, Central Asia and Asian Russia since the end the 1960s. Landscape research in the Anthropocene has to focus on achieving landscape sustainability at high productivity. This includes halting landscape degradation, developing cultural landscapes, and maintaining semi-natural landscapes. Clean water and air, fertile and healthy soils for food and other ecosystem services and a green and bio-diverse environment are attributes of landscapes for the survival and well-being of humans. Research has to generate knowledge, innovations and decision rules by disciplinary, interdisciplinary and trans-disciplinary work. Knowledge generation in a globalized world is based on big data gathering and scenario modelling. International long-term experiments and agri-environmental monitoring systems will deliver data for ecosystem models and decision support systems.

This paper utilizes cointegration theory, error correcting model and Granger causality testing theory to make an empirical research on the relation between urbanization and GDP in China, and also implements a comparative analysis to the relation between three industries and degree of urbanization, the related coeffecient is 0.97, 0.95, 0.97, 0.97. And the result shows a long-term balance between these two factors, and the promoting effect to tertiary industry by urbanization is more obvious. Urbanization and economic growth are the long-term balanced relations. In the long-term balance, every 1% increment of urbanization can make 4.82% increment of GDP; In short-term balance, if the balance depart from the long-term balance at the i-th term, the model will take automatic reversal adjustment with -0.06 adjusting strength at the (i+1)th term, to make it move to the long-term balance. The economic growth onto urbanization is one-way causality relationship, the primary and secondary industry onto urbanization is also one-way causality relationship. However, the tertiary industry onto urbanization is both-way causality relationship.

This dataset includes annual soil conservation capacities and their impact factors in China from 1992 to 2019. These data are developed based on an improved RUSLE model to estimate potential and controlled soil erosion in China from 1992 to 2019. As important input factors, the vegetation cover and management (C) factor and rainfall erosivity (R) factor are optimized for different regions. The C-factor is optimized according to each province's farmland and non-farmland conditions. The R-factor is calculated for karst and non-karst areas separately using daily precipitation. The dataset contains nine zip files (“.rar”), which can be divided into comprehensive data and detailed data. Comprehensive data include mean values and changing rates of soil conservation capacity (SC1992-2019), the C-factor (C1992-2019), and the R-factor (R1992-2019) in China from 1992 to 2019. Detailed data include the water and soil conservation measure factor data (P_300), the soil erodibility factor data (K_300), the topographic factor data (LS_300), the R-factor data in two-year increments (R_year), the C-factor data in two-year increments (C_year), and the SC data in two-year increments (SC_year). Most data have a spatial resolution of 300 m (the resolution of the R-factor is 1 km). All the data in the zip files are raster data (“.tif”), which can be opened by GIS software like ArcMap. This dataset can support large-scale and long-term assessment of soil and water conservation potential in China. It also can serve as a basis for identifying the impacts of climate change and human activities on soil conservation services. This dataset includes annual soil conservation capacities and their impact factors in China from 1992 to 2019. These data are developed based on an improved RUSLE model to estimate potential and controlled soil erosion in China from 1992 to 2019. As important input factors, the vegetation cover and management (C) factor and rainfall erosivity (R) factor are optimized for different regions. The C-factor is optimized according to each province's farmland and non-farmland conditions. The R-factor is calculated for karst and non-karst areas separately using daily precipitation. The dataset contains nine zip files (“.rar”), which can be divided into comprehensive data and detailed data. Comprehensive data include mean values and changing rates of soil conservation capacity (SC1992-2019), the C-factor (C1992-2019), and the R-factor (R1992-2019) in China from 1992 to 2019. Detailed data include the water and soil conservation measure factor data (P_300), the soil erodibility factor data (K_300), the topographic factor data (LS_300), the R-factor data in two-year increments (R_year), the C-factor data in two-year increments (C_year), and the SC data in two-year increments (SC_year). Most data have a spatial resolution of 300 m (the resolution of the R-factor is 1 km). All the data in the zip files are raster data (“.tif”), which can be opened by GIS software like ArcMap. This dataset can support large-scale and long-term assessment of soil and water conservation potential in China. It also can serve as a basis for identifying the impacts of climate change and human activities on soil conservation services.

Collectively, reservoirs created by dams are thought to be an important source of greenhouse gases (GHGs) to the atmosphere. So far, efforts to quantify, model, and manage these emissions have been limited by data availability and inconsistencies in methodological approach. Here, we synthesize reservoir CH4, CO2, and N2O emission data with three main objectives: (1) to generate a global estimate of GHG emissions from reservoirs, (2) to identify the best predictors of these emissions, and (3) to consider the effect of methodology on emission estimates. We estimate that GHG emissions from reservoir water surfaces account for 0.8 (0.5–1.2) Pg CO2 equivalents per year, with the majority of this forcing due to CH4. We then discuss the potential for several alternative pathways such as dam degassing and downstream emissions to contribute significantly to overall emissions. Although prior studies have linked reservoir GHG emissions to reservoir age and latitude, we find that factors related to reservoir productivity are better predictors of emission. Reservoir Greenhouse Gas Fluxes and Potential Predictor Variables This data file contains reservoir greenhouse gas emission estimates as well as categorical and continuous data for tested predictors of these fluxes. There is one row reserved for each reservoir included in the study. The associated references for this data are included in a second spreadsheet tab.

Plant secondary metabolites (SMs) play crucial roles in plant-environment interactions and contribute greatly to human health. Global climate changes are expected to dramatically affect plant secondary metabolism, yet a systematic understanding of such influences is still lacking. Here, we employed medicinal and aromatic plants (MAAPs) as model plant taxa and performed a meta-analysis from 360 publications using 1828 paired observations to assess the responses of different SMs levels and the accompanying plant traits to elevated carbon dioxide (eCO2), elevated temperature (eT), elevated nitrogen deposition (eN), and decreased precipitation (dP). The overall results showed that phenolic and terpenoid levels generally respond positively to eCO2 but negatively to eN, while the total alkaloid concentration was increased remarkably by eN. By contrast, dP promotes the levels of all SMs, while eT exclusively exerts a positive influence on the levels of phenolic compounds. Further analysis highlighted the dependence of SM responses on different moderators such as plant functional types, climate change levels or exposure durations, mean annual temperature and mean annual precipitation. Moreover, plant phenolic and terpenoid responses to climate changes could be attributed to the variations in C/N ratio and total soluble sugar levels, while the trade-off supposition contributed to SM responses to climate changes other than eCO2. Taken together, our results predicted the distinctive SM responses to diverse climate changes in MAAPs, and allowed us to define potential moderators responsible for these variations. Further, linking SM responses to C-N metabolism and growth-defence balance provided biological understandings in terms of plant secondary metabolic regulation. Peer-reviewed journal articles published online from January 1990 to March 2022 were searched using Web of Science (http://www.isiknowledge.com/), with the following terms: (global change OR climate change OR free-air carbon dioxide enrichment OR free-air CO2 enrichment OR elevated carbon dioxide OR elevated CO2 OR elevated atmospheric CO2 OR CO2 enrichment OR eCO2 OR atmospheric CO2 enrichment OR elevated atmospheric carbon dioxide OR carbon dioxide enrichment OR [carbon dioxide] OR nitrogen deposition OR nitrogen addition OR nitrogen application OR nitrogen fertiliz* OR nitrogen nutrition OR N deposition OR N addition OR N application OR N fertiliz* OR N nutrition OR changing precipitation OR increased precipitation OR decreased precipitation OR drought OR water stress OR water addition OR warming OR elevated temperature OR climate warming OR elevated temperature OR increased temperature) AND (medicinal plant OR aromatic plants). 

The dataset contains 7 figures and 12 tables of the manuscript. The dataset contains 7 figures and 12 tables of the manuscript.

Water samples 20 cm below the water surface were collected once a day in all treatments. TP concentrations in the water column were measured using combined persulfate digestion. The optical density 680 (OD680) value, which represents the algal density, was measured at a wavelength of 680 nm using an ultraviolet–visible spectrophotometer. After 30 days, intact plants were collected and washed for laboratory determination of indicators. Each individual plant was carefully collected, and the epiphyton was separated from the macrophyte using a small brush in the laboratory. Filamentous algae were collected with a scraper from the walls of the aquariums and dried naturally on foil. Weight and root/shoot were expressed in fresh weight. Peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) were extracted from fresh V. natans tissues. The enzymatic content of M. spicatum was not analyzed, due to insufficient fresh weight for grinding. POD activity was determined by an increase in absorbance at a wavelength of 470 nm due to guaiacol oxidation. The unit of CAT activity was defined as the amount of enzyme that decomposed 1 μmol H2O2 per minute . SOD activity was measured using the method described by Ewing and Janero. We obtained 3 mL water samples (20 cm below the water surface) from the experimental aquariums each day. The samples were dark-acclimated for 15 minutes. The minimum fluorescence (F0) value and the maximum fluorescence (Fm) value were measured by a Phyto-PAM (Walz, Germany). The difference between the Fm and F0 values is the variable fluorescence (Fv). Then the maximum photochemical efficiency of photosystem II (Fv/Fm) was measured.  Due to climate change and increasing anthropogenic activities, lakes are disturbed frequently, usually by press (e.g., diffused pollution, rising temperatures) or pulse (e.g., storms, rainfall, pollution events) disturbances. Both press and pulse disturbances can affect abiotic and biotic environments, changing the structure of ecosystems and affecting ecosystem services. To confront with the effects of climate change and increasing anthropogenic activities, understanding the different effects of press and pulse disturbances on lake ecosystems is essential. This study assessed the effect of press and pulse disturbances of phosphorus on a microcosmic aquatic ecosystem by measuring the total phosphorus (TP), algae density, and physiological indicators of submerged macrophytes. We found that the microcosmic aquatic ecosystem responded differently to press and pulse disturbances. Our results suggested that it had a lower resistance to pulse phosphorus disturbances than to press phosphorus disturbances. There were significantly higher nutrient concentrations and algal densities in the pulse treatment than in the press treatment. Positive feedback was found between the biomass of submerged macrophytes and the water quality. There was a higher submerged macrophytes biomass at low TP concentration and algal density. In the context of climate change, press and pulse disturbances could have severe impacts on lake ecosystems. Our findings will provide some insight for further research and lake management.

This dataset records the Spatiotemporal Variation of early rice phenology, contains the details of early rice agrometeorological experiment stations and mean date of early rice phenology date and trend in phenology date during the period of 1981–2009. This dataset is helpful to study the response of early rice to climate change. This dataset records the Spatiotemporal Variation of early rice phenology, contains the details of early rice agrometeorological experiment stations and mean date of early rice phenology date and trend in phenology date during the period of 1981–2009. This dataset is helpful to study the response of early rice to climate change.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022).