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Alpine tundra, covering 3% of the Earth's land surface, harbors approximately 4% of higher plant species. Changes in this vegetation significantly impact biodiversity and ecosystem services. Recent studies have primarily focused on large-scale and long-term vegetation changes in polar and high-latitude regions. However, the study of short-term vegetation changes and their primary drivers has received insufficient attention in alpine tundra. This study aimed to investigate vegetation changes and their dominant drivers in the alpine tundra of Changbai Mountains-located at the southern edge of the alpine tundra distribution in Eastern Eurasia-over a short period by re-surveying permanent plots in 2019 and comparing them with data from 2014. The results showed that significant changes were observed in alpine tundra vegetation during the study period. The importance values of typical alpine tundra plants such as Rhododendron chrysanthum , Vaccinium uliginosum , and Dryas octopetala decreased noticeably, while those of herbaceous species such as Deyeuxia angustifolia and Sanguisorba sitchensis increased significantly. Species richness, diversity, and evenness at different altitudinal gradients showed varying degrees of increase. A distinct expansion trend of herbaceous species was observed in the alpine tundra, contributing to a shift in plant community composition toward herbaceous dominance. This shift might result in the meadowization of the dwarf shrub tundra. Our findings further revealed that soil nutrients rather than climate factors, dominated the changes of plant communities over a short period. These findings provide scientific references for the conservation and management of biodiversity, as well as for projecting future vegetation dynamics in alpine tundra., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xing, Wang, Wang, Du, Wu, Zong, Cong and Ba.)

Understanding the probability distributions of precipitation is crucial for predicting climatic events and constructing hydraulic facilities. To overcome the inadequacy of precipitation data, regional frequency analysis was commonly used by "trading space for time". However, with the increasing availability of gridded precipitation datasets with high spatial and temporal resolutions, the probability distributions of precipitation for these datasets have been less explored. We used L-moments and goodness-of-fit criteria to identify the probability distributions of annual, seasonal, and monthly precipitation for a 0.5° × 0.5° dataset across the Loess Plateau (LP). We examined five 3-parameter distributions, namely General Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), and Pearson type III (PE3), and evaluated the accuracy of estimated rainfall using the leave-one-out method. We also presented pixel-wise fit-parameters and quantiles of precipitation as supplements. Our findings indicated that precipitation probability distributions vary by location and time scale, and the fitted probability distribution functions are reliable for estimating precipitation under various return periods. Specifically, for annual precipitation, GLO was prevalent in humid and semi-humid areas, GEV in semi-arid and arid areas, and PE3 in cold-arid areas. For seasonal precipitation, spring precipitation mainly conforms to GLO distribution, summer precipitation around the 400 mm isohyet prevalently follows GEV distribution, autumn precipitation primarily meets GPA and PE3 distributions, and winter precipitation in the northwest, south, and east of the LP mainly conforms to GPA, PE3 and GEV distributions, respectively. Regarding monthly precipitation, the common distribution functions are PE3 and GPA for the less-precipitation months, whereas the distribution functions of precipitation for more-precipitation months vary substantially across different regions of the LP. Our study contributes to a better understanding of precipitation probability distributions in the LP and provides insights for future studies on gridded precipitation datasets using robust statistical methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Droughts are widespread in China and have brought considerable losses to the economy and society. Droughts are intricate, stochastic processes with multi-attributes (e.g., duration, severity, intensity, and return period). However, most drought assessments tend to focus on univariate drought characteristics, which are inadequate to describe the intrinsic characteristics of droughts due to the existence of correlations between drought attributes. In this study, we employed the standardized precipitation index to identify drought events using China's monthly gridded precipitation dataset from 1961 to 2020. Univariate and copula-based bivariate methods were then used to examine drought duration and severity on 3-, 6-, and 12-month time scales. Finally, we used the hierarchical cluster method to identify drought-prone regions in mainland China at various return periods. Results revealed that time scale played an essential role in the spatial heterogeneity of drought behaviors, such as average characteristics, joint probability, and risk regionalization. The main findings were as follows: (1) 3- and 6-month time scales yielded comparable regional drought features, but not 12-month time scales; (2) higher drought severity was associated with longer drought duration; (3) drought risk was higher in the northern Xinjiang, western Qinghai, southern Tibet, southwest China, and the middle and lower reaches of the Yangtze River, and lower in the southeastern coastal areas of China, the Changbai Mountains, and the Greater Khingan Mountains; (4) mainland China was divided into six subregions according to joint probabilities of drought duration and severity. Our study is expected to contribute to better drought risk assessment in mainland China.

As the Earth's population continuously increase with the passage of time, the demand for agricultural raw material for human need increases. It is critical to maintaining updated and accurate information about the dynamics and properties of the world agricultural systems. As cash crop, the updated information of the spatial distribution of cotton field is necessary to monitor the crop area and growth changes at regional level. We used 8-day enhanced vegetation index (EVI) time series to detect cotton crop area and binomial probabilistic approach to obtain the probability distribution of cotton crop occurrence. We used Gaussian kriging to derive cotton yield inside the detected cotton crop areas through crop reporting data. We also used field data from farmers to validate the cotton yield results. A strong correlation between the MODIS-derived cotton cultivated area and statistical data at the tehsil level were achieved (R 2  = 0.84) for all study years (2004-2019). The total accuracy for the cotton crop area detection was 84.6% and yield prediction was 92.1%. Our study presents new approaches to map cotton area and yield, which are applicable to other regions through machine learning., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil microbial biomass and microbial stoichiometric ratios are important for understanding carbon and nutrient cycling in terrestrial ecosystems. Here, we compiled data from 12245 observations of soil microbial biomass from 1626 published studies to map global patterns of microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), and their stoichiometry using a random forest model. Concentrations of MBC, MBN, and MBP were most closely linked to soil organic carbon, while climatic factors were most important for stoichiometry in microbial biomass ratios. Modeled seasonal MBC concentrations peaked in summer in tundra and in boreal forests, but in autumn in subtropical and in tropical biomes. The global mean MBC/MBN, MBC/MBP, and MBN/MBP ratios were estimated to be 10, 48, and 6.7, respectively, at 0-30 cm soil depth. The highest concentrations, stocks, and microbial C/N/P ratios were found at high latitudes in tundra and boreal forests, probably due to the higher soil organic matter content, greater fungal abundance, and lower nutrient availability in colder than in warmer biomes. At 30-100 cm soil depth, concentrations of MBC, MBN, and MBP were highest in temperate forests. The MBC/MBP ratio showed greater flexibility at the global scale than did the MBC/MBN ratio, possibly reflecting physiological adaptations and microbial community shifts with latitude. The results of this study are important for understanding C, N, and P cycling at the global scale, as well as for developing soil C-cycling models including soil microbial C, N, and P as important parameters., (© 2022 John Wiley & Sons Ltd.)

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km 2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km 2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Climate change and other global change drivers threaten plant diversity in mountains worldwide. A widely documented response to such environmental modifications is for plant species to change their elevational ranges. Range shifts are often idiosyncratic and difficult to generalize, partly due to variation in sampling methods. There is thus a need for a standardized monitoring strategy that can be applied across mountain regions to assess distribution changes and community turnover of native and non-native plant species over space and time. Here, we present a conceptually intuitive and standardized protocol developed by the Mountain Invasion Research Network (MIREN) to systematically quantify global patterns of native and non-native species distributions along elevation gradients and shifts arising from interactive effects of climate change and human disturbance. Usually repeated every five years, surveys consist of 20 sample sites located at equal elevation increments along three replicate roads per sampling region. At each site, three plots extend from the side of a mountain road into surrounding natural vegetation. The protocol has been successfully used in 18 regions worldwide from 2007 to present. Analyses of one point in time already generated some salient results, and revealed region-specific elevational patterns of native plant species richness, but a globally consistent elevational decline in non-native species richness. Non-native plants were also more abundant directly adjacent to road edges, suggesting that disturbed roadsides serve as a vector for invasions into mountains. From the upcoming analyses of time series, even more exciting results can be expected, especially about range shifts. Implementing the protocol in more mountain regions globally would help to generate a more complete picture of how global change alters species distributions. This would inform conservation policy in mountain ecosystems, where some conservation policies remain poorly implemented., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes., (© 2020 John Wiley & Sons Ltd.)

Regions at high latitudes and high altitudes are undergoing a more pronounced winter warming than spring warming, and such asymmetric warming will affect chilling and forcing processes and thus the spring phenology of plants. We analyzed winter chilling and spring forcing accumulation in relation to the spring phenology of three tree species (Ulmus pumila, Populus simonii, and Syringa oblata) growing in a cold region (CR) compared with trees in a warmer reference region (WR), using the Dynamic Model and the Growing Degree Hour (GDH) model. We tested that forcing rather than chilling affects the spring phenology of trees in CR (hypothesis I), and that trees in CR have both lower mean chilling and forcing temperature and thus longer accumulation periods than trees in WR (hypothesis II). The modeling results confirmed that chilling and forcing occur simultaneously during the early spring when temperature gradually increases. In line with our hypotheses, forcing played a crucial role in spring phenology in CR, but chilling and forcing combined to determine spring phenology in WR. The temperature during the chilling and forcing periods was lower and the accumulation period started earlier and ended later in CR than in WR. Moreover, the chilling accumulation was broken into two periods by the low deep winter temperature in CR, and that interruption will be removed by future strong winter warming. Future asymmetric warming, with a stronger temperature increase in winter than in spring, could decrease the forcing accumulation effects and increase the chilling effects on the spring phenology of plants in CR. This change in the balance between chilling and forcing will lead to a shift in plant phenology, which will further have major impacts on biogeochemical cycles and on ecosystem functions and services., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Environmental factors that drive carbon storage are often used as an explanation for alpine treeline formation. However, different tree species respond differently to environmental changes, which challenges our understanding of treeline formation and shifts. Therefore, we selected Picea jezoensis and Betula ermanii , the two treeline species naturally occurring in Changbai Mountain in China, and measured the concentration of non-structural carbohydrates (NSC), soluble sugars and starch in one-year-old leaves, shoots, stems and fine roots at different elevations. We found that compared with P. jezoensis , the NSC and soluble sugars concentrations of leaves and shoots of B. ermanii were higher than those of P. jezoensis , while the starch concentration of all the tissues were lower. Moreover, the concentration of NSC, soluble sugars and starch in the leaves of B. ermanii decreased with elevation. In addition, the starch concentration of B. ermanii shoots, stems and fine roots remained at a high level regardless of whether the soluble sugars concentration decreased. Whereas the concentrations of soluble sugars and starch in one-year-old leaves, shoots and stems of P. jezoensis responded similarly changes with elevation. These findings demonstrate that compared with P. jezoensis , B. ermanii has a higher soluble sugars/starch ratio, and its shoots, stems and fine roots actively store NSC to adapt to the harsh environment, which is one of the reasons that B. ermanii can be distributed at higher altitudes.

Vegetation in the alpine tundra area of the Changbai Mountains, one of two alpine tundra areas in China, has undergone great changes in recent decades. The aggressive herb species Deyeuxia angustifolia (Komarov) Y. L. Chang, a narrow-leaf small reed, was currently encroaching upon the alpine landscape and threatening tundra biota. The alpine tundra of the Changbai Mountains has been experiencing a warmer climate and receiving a high load of atmospheric nitrogen deposition. In this study, we aimed to assess the respective roles of climate warming and atmospheric nitrogen deposition in promoting the upward encroachment of D. angustifolia. We conducted experiments for three years to examine the response of D. angustifolia and a native alpine shrub, Rhododendron chrysanthum, to the conditions in which temperature and nitrogen were increased. Treatments consisting of temperature increase, nitrogen addition, temperature increase combined with nitrogen addition, and controls were conducted on the D. angustifolia communities with three encroachment levels (low, medium, and high levels). Results showed that 1) D. angustifolia grew in response to added nutrients but did not grow well when temperature increased. R. chrysanthum showed negligible responses to the simulated environmental changes. 2) Compared to R. chrysanthum, D. angustifolia could effectively occupy the above-ground space by increasing tillers and growing rapidly by efficiently using nitrogen. The difference in nitrogen uptake abilities between the two species contributed to expansion of D. angustifolia. 3) D. angustifolia encroachment could deeply change the biodiversity of tundra vegetation and may eventually result in the replacement of native biota, especially with nitrogen addition. Our research indicated that nutrient perturbation may be more important than temperature perturbation in promoting D. angustifolia encroachment upon the nutrient- and species-poor alpine tundra ecosystem in the Changbai Mountains., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Tree line ecotone in the Changbai Mountains has undergone large changes in the past decades. Tree locations show variations on the four sides of the mountains, especially on the northern and western sides, which has not been fully explained. Previous studies attributed such variations to the variations in temperature. However, in this study, we hypothesized that topographic controls were responsible for causing the variations in the tree locations in tree line ecotone of the Changbai Mountains. To test the hypothesis, we used IKONOS images and WorldView-1 image to identify the tree locations and developed a logistic regression model using topographical variables to identify the dominant controls of the tree locations. The results showed that aspect, wetness, and slope were dominant controls for tree locations on western side of the mountains, whereas altitude, SPI, and aspect were the dominant factors on northern side. The upmost altitude a tree can currently reach was 2140 m asl on the northern side and 2060 m asl on western side. The model predicted results showed that habitats above the current tree line on the both sides were available for trees. Tree recruitments under the current tree line may take advantage of the available habitats at higher elevations based on the current tree location. Our research confirmed the controlling effects of topography on the tree locations in the tree line ecotone of Changbai Mountains and suggested that it was essential to assess the tree response to topography in the research of tree line ecotone.