Search

As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

This study evaluates how resilient farmers' livelihoods are to climate change and what factors influence this resilience. To measure resilience, we constructed an indicator system based on the livelihood resilience analysis framework. We surveyed 42 experts and 630 farmers after a climate change disturbance in Aohan Banner, Inner Mongolia, from August to October 2021, and analyzed these data using the comprehensive index method. Meanwhile, we used a multiple linear regression model to analyze the key factors affecting farmer livelihood resilience across different livelihood types and towns. We found that farmers who primarily worked in agriculture had the highest resilience scores and that livelihood resilience differed by geographical location; specifically, livelihood resilience gradually declines from southern to northern areas and from forest and forest-grassland to grassland locations. The results also show that education level, agricultural technology training, transportation infrastructure, accessibility of information, awareness of climate change, climate change perception, change in livelihood strategies, family size, and the holding size of the arable area are positively associated with farmer livelihood resilience, while household head age is negatively associated with resilience. We therefore advise that policymakers should diversify agricultural livelihoods, afforest surrounding arable areas, improve transportation infrastructure, increase learning activities and skill training for farmers, and publicize climate change knowledge. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China's Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the terrestrial water storage (TWS) changes and the major driving factors have been hindered by the lack of direct observations in Inner Mongolia, China. In this study, the spatial and temporal changes of TWS and groundwater storage (GWS) in Inner Mongolia during 2003–2021 were evaluated using the satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow On combined with data from land surface models. The results indicated that Inner Mongolia has experienced a widespread TWS loss of approximately 1.82 mm/yr from 2003–2021, with a more severe depletion rate of 4.15 mm/yr for GWS. Meteorological factors were the driving factors for water storage changes in northeastern and western regions. The abundant precipitation increased TWS in northeast regions at 2.36 mm/yr. Anthropogenic activities (agricultural irrigation and coal mining) were the driving factors for water resource decline in the middle and eastern regions (especially in the agropastoral transitional zone), where the decrease rates were 4.09 mm/yr and 3.69 mm/yr, respectively. In addition, the severities of hydrological drought events were identified based on water storage deficits, with average severity values of 17 mm, 18 mm, 24 mm, and 33 mm for the west, middle, east, and northeast regions, respectively. This study established a basic framework for water resource changes in Inner Mongolia and provided a scientific foundation for further water resources investigation. [ABSTRACT FROM AUTHOR]

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation. [ABSTRACT FROM AUTHOR]

Ephedra sinica Stapf is an important traditional medicinal plant. However, in recent years, due to climate change and human activities, its habitat area and distribution area have been decreasing sharply. In order to provide better protection for E. sinica, it is necessary to study the historical and future potential zoning of E. sinica. The maximum entropy model (MaxEnt) was used to simulate the potential geographical distribution patterns of E. sinica under historical and future climatic conditions simulated using two Shared Socio-economic Pathways. The main results were also analyzed using the jackknife method and ArcGIS. The results showed that: (1) the potential suitable distribution area of E. sinica in China is about 29.18 × 105 km2—high-suitable areas, medium-suitable areas, and low-suitable areas cover 6.38 × 105 km2, 8.62 × 105 km2, 14.18 × 105 km2, respectively—and E. sinica is mainly distributed in Inner Mongolia; (2) precipitation and temperature contribute more to the distribution of E. sinica; (3) under two kinds of SSPs, the total suitable area of E. sinica increased significantly, but the differences between 2021–2040, 2041–2060, 2061–2080, and 2081–2100 are not obvious; (4) the barycentre of E. sinica moves from the historical position to its southwest. The results show that E. sinica can easily adapt to future climates well, and its ecological value will become more important. This study provides scientific guidance for the protection, management, renewal and maintenance of E. sinica. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

The impact of global climate change on ecosystem structure has attracted much attention from researchers. However, how climate change and meteorological conditions influence community phylogenetic structure remains poorly understood. In this research, we quantified the responses of grassland communities' phylogenetic structure to long- and short-term meteorological conditions in Inner Mongolia, China. The net relatedness index (NRI) was used to characterize phylogenetic structure, and the relationship between the NRI and climate data was analyzed to understand the dynamics of community phylogenetic structure and its relationship with extreme meteorological events. Furthermore, multiple linear regression and structural equation models (SEMs) were used to quantify the relative contributions of meteorological factors before and during the current growing season to short-term changes in community phylogenetic structure. In addition, we evaluated the effect of long-term meteorological factors on yearly NRI anomalies with classification and regression trees (CARTs). We found that 1) the degree of phylogenetic clustering of the community is relatively low in the peak growing season, when habitat filtering is relatively weak and competition is fiercer. 2) Extreme meteorological conditions (i.e., drought and cold) may change community phylogenetic structure and indirectly reduce the degree of phylogenetic clustering by reducing the proportion of dominant perennial grasses. 3) Meteorological conditions before the growing season rather than during the current growing season explain more variation in the NRI and interannual NRI anomalies. Our results may provide useful information for understanding grassland community species assembly and how climate change affects biodiversity. [ABSTRACT FROM AUTHOR]

Global surface temperature is increasing significantly under climate change with increasingly uneven distribution of precipitation, which has been making an important impact on the sustainable development of the dryland agriculture in semi-arid regions. The Inner Mongolia Autonomous Region (IMAR) has the largest area ratio of semi-arid regions in China, and spring wheat is one of its major grain crops, and the negative impacts of climate change pose a serious threat to its production. In order to reduce this threat, we assessed the impact thresholds and risks posed by the main climatic factors to spring-wheat production, and put forward quantitative adaptive countermeasures. The results showed that, from 1961 to 2012, the appropriate impact thresholds of average temperature, precipitation, fertilization level, and water supply level were 15.6 °C, 333 mm, 319.5 kg/ha, and 743.7 mm, respectively. Compared with the average values in 1996–2012, the maximum fertilization level and water supply level could be increased by 162.2 kg/ha and 406.5 mm, respectively, to improve the adaptive yield of spring wheat. The risks of adverse impacts of temperature rise, precipitation decrease, and their combined effect on spring-wheat production were found to be 11.1%, 9.3%, and 10.3%, respectively. The whole IMAR showed a high risk of adverse impacts of temperature rise and the combined action of temperature rise and precipitation decrease on spring-wheat production. In particular, the spring-wheat production in the northeastern, eastern, and central areas were found to be at the highest risk to the impacts of climate change. Based on the determined appropriate thresholds of fertilization level and water supply level, it was determined that in the northeastern area, the maximum fertilization level and water supply level could be increased by 229.9 kg/ha and 441.4 mm, respectively, to increase the adaptive yield of spring wheat. In the eastern area, the two factors could be increased by 142.2 kg/ha and 464.3 mm, respectively. In the central area, they could be increased by 242.2 kg/ha and 513.7 mm, respectively. In the southwestern area, they could be increased by 41.4 kg/ha and 201.7 mm, respectively. [ABSTRACT FROM AUTHOR]

Global climate change is altering the amounts of ice and snow in winter, and this could be a major driver of soil microbial processes. However, it is not known how bacterial and fungal communities will respond to changes in the snow cover. We conducted a snow manipulation experiment to study the effects of snow removal on the diversity and composition of soil bacterial and fungal communities. A snow manipulation experiment was carried out on the meadow steppe in Hulunbuir, Inner Mongolia, China, during the winter period October 2019–March 2020. Soil samples were collected from the topsoil (0–10 cm) in mid-March 2020 (spring snowmelt period). Snow removal significantly reduced soil moisture and soil ammonium concentration. Lower snow cover also significantly changed the fungal community structure and beta diversity. Snow removal did not affect the bacterial community, indicating that fungal communities are more sensitive to snow exclusion than bacterial communities. The relative importance analysis (using the Lindeman–Merenda–Gold method) showed that available nitrogen (AN), soil water content (SWC), total organic carbon (TOC), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) together explained 94.59% of the variation in soil fungal beta diversity, where AN was identified as the most important predictor. These finding provide insights into potential impacts of climate warming and associated reduced snow cover on soil microbial communities and processes. [ABSTRACT FROM AUTHOR]

Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods. Plain Language Sumamry: To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales (e.g., century timescale). The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past one century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the one‐century water storage changes and investigated the relationships with hydrological and climatic variables in long term. The century‐scale reconstruction result reveals that the water storage reached the highest point in the 1960s within the period 1910‐2019. The lowest stage occurred in the 1930s‐1940s, which was even lower than the alarted shrinkage stage in 2012. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, wet day frequency, potential evapotranspiration, and temperature. However, the dominating characteristics of different variables vary evidently with different sub‐periods. This study is expected to provide an efficient technical solution of reconstructing long‐term lake water storage records and to advance our scientific understanding of the characteristics of lake water balances in response to climate change and variability in the Mongolia Plateau. Key Points: The complete bathymetry map of Lake Hulun was constructed based multi‐source remote sensing dataA machine learning approach was employed to reconstruct the century‐scale water storage of Lake HulunThe potential links of one‐century variations of lake water storage with climatic variables and atmospheric circulations [ABSTRACT FROM AUTHOR]

Unanticipated environmental shocks impact the livelihoods of many resource users around the world. These shocks are likely to be more unpredictable as the effects of climate change continue to mount. Yet how households adapt to these changing climatic conditions especially in the context of rapidly changing market conditions in most areas of the world, is poorly understood. These interactions have wide implications for both smallholder livelihoods and sustainable use of natural resources. In this paper, we examine the relationship between environmental shocks and stocking rates in livestock herds in the Inner Mongolian grasslands of northern China. We uniquely examine three types of shocks and how households adapt livestock production strategies in response to each. Separately and in aggregate, we look at three common shocks in Inner Mongolia: droughts, snowstorms, and locust outbreaks. We use a difference-in-differences approach to estimate changes in stocking rates among households that experience shocks versus those that do not with a panel dataset from 2009 to 2014 of 597 households. While we find no clear impact from locusts, our results suggest droughts and snowstorms have opposite effects: droughts are associated with increases in herd sizes, but snowstorms result in decreased herds. We suggest these differences are due to interactions between shocks, emerging options to borrow on credit, and livestock markets. Household adaptation to climate change will be strategic and take advantage of both available resources as well as market conditions. • Climate change is causing stronger and more unpredictable environmental shocks. • We use a difference-in-differences model to estimate the impact of shocks on stocking rate. • Drought relates to bigger herds; snowstorms relate to smaller herds. • Households adapt to shocks taking advantage of both available resources and market conditions. • Shaping well-functioning market may reduce opportunistic resource exploitation. [ABSTRACT FROM AUTHOR]

Lophialetidae is an extinct group of endemic Asiatic tapiroids that are widely distributed in the Eocene sediments of Asia. Schlosseria magister and Lophialetes expeditus are the most abundant species in this family. However, their dietary and ecological characteristics are largely unknown. For the first time, we reconstruct the palaeodiet and habitat of these two lophialetids using a combination of mesowear and stable carbon isotope analysis of fossil teeth excavated from the Erlian Basin, China. Mesowear analysis (n = 141) suggests that the dietary structure of S. magister and L. expeditus shifted from less to more abrasive diets from ~52 to ~42 Ma. Stable carbon isotope analysis (n = 137) suggests that the habitats of S. magister and L. expeditus became drier and/or more open through time. The dietary shifts of the two lophialetids are consistent with evident changes in habitat. The changes in the diet and habitat were probably related to global climate change during that time period. The gradual drop in global temperatures during the early–middle Eocene led to a drier and more open terrestrial ecosystem in the Erlian Basin, probably resulting in changes in floral composition of the environment inhabited by S. magister and L. expeditus. Hence, herbivores highly susceptible to vegetation modification had to develop new resource exploitation strategies to adapt to these changes. Schlosseria magister, considered to be the sister‐group of L. expeditus and with a low level of ecological flexibility, was unable to adapt to the habitat changes finally becoming extinct at ~45 Ma. [ABSTRACT FROM AUTHOR]

Climate change has become a global concern for scientists as it is affecting almost every ecosystem. Larix gmelinii and Betula platyphylla are native and dominant forest species in the Daxing'anling Mountains of Inner Mongolia, playing a major role in carbon sequestration of this region. This study was carried out to assess the effect of climate variables including precipitation and temperature on the biomass of Larix gmelinii and Betula platyphylla forests. For this purpose, we used the climate-sensitive stem biomass allometric model for both species separately to find out accurate stem biomass along with climatic factors from 1950 to 2016. A total of 66 random plots were taken to attain the data from this study area. Larix gmelinii and Betula platyphylla stem biomass have a strong correlation with annual precipitation (R2 = 0.86, R2 = 0.71, R2 = 0.79, and R2 = 0.59) and maximum temperature (R2 = 0.76, R2 = 0.64, R2 = 0.67, and R2 = 0.52). However, annual minimum temperature (R2 = 0.58, R2 = 0.43, R2 = 0.55, and R2 = 0.46) and annual mean temperature (R2 = 0.40, R2 = 0.22, R2 = 0.36, and R2 = 0.19) have a relatively negative impact on tree biomass. Therefore, we suggest that both species have a very strong adaptive nature with climatic variation and hence can survive under drought and high-temperature stress climatic conditions. [ABSTRACT FROM AUTHOR]

Exploring possible relationships between climate-induced maize yield and rainy-season precipitation under climate change is fundamental to science-based decision for food security in Inner Mongolia Autonomous Region (IMAR). To determine the relationship of maize yield with precipitation variables across IMAR during 1960-2012, we selected 11 precipitation indices of rainy season and divided IMAR into four sub-regions-west, middle, east, and northeast using principal component analysis and K-means clustering methods. Results show that climate-induced maize yield is more sensitive to precipitation variability in the west and middle IMAR. The most important precipitation factor that limits maize yield is moderate precipitation days in these two sub-regions. Moreover, west and middle Inner Mongolia was dominated by decreasing precipitation during the rainy season. Furthermore, the El Niño/La Niña-Southern Oscillation cycle has the significant influence on the rainy-season precipitation in the west and middle IMAR. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic activities are widely considered the main factors affecting vegetation growth. However, their relative contributions are under debate. Within the non-climatic impact, detailed human activities, particularly government policy adjustments, are less investigated. In this study, we develop a fractional vegetation coverage (FVC) extraction method based on MODIS-EVI satellite data to analyze the spatiotemporal variation of vegetation and its attributions in the China–Mongolia–Russia Economic Corridor (CMREC). The average FVC has improved, with a general increase of 0.02/10a from 2000 to 2020. We construct a driving factor identification system for FVC change, based on partial and multiple correlation coefficients, and we divide the driving forces of FVC changes into seven climate-driven types and one non-climate-driven type. The results reveal that FVC changes caused by climatic factors account for 28.2% of CMREC. The most prominent greening (19.5%) is precipitation-driven, and is extensively distributed in Khentii Aimag, Mongolia; southeast Inner Mongolia; west Jilin Province; and southwest Heilongjiang Province, China. Moreover, we quantify the relative contribution of climatic and non-climatic factors to significant FVC change using the first-difference multivariate regression method. The results indicate that the effects of non-climatic factors on vegetation change outweigh those of climatic factors in most areas. According to the land cover change and regional policy adjustment, anthropogenic activities such as afforestation, reclamation, and planting structure adjustment explain most vegetation improvement in the Northeast Plain; eastern Inner Mongolia; and the Hetao Irrigation District, China. Meanwhile, both vegetation improvement and degradation disperse concurrently in the Mongolian and Russian parts of CMREC, where climate change and anthropogenic activities positively and negatively affect vegetation change, respectively. Despite the greening in most CMREC, it must be noted that human-induced greening is unsustainable to some degree. The overdevelopment of black soil area and sandy land, adverse effects of afforestation projects, and natural hazards related to weather and climate extremes altogether threaten the local ecological security in the long run. Therefore, governments should develop new desertification countermeasures in accordance with the laws of nature, and enhance international cooperation to guarantee the ecological safety of CMREC. [ABSTRACT FROM AUTHOR]

Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence. [ABSTRACT FROM AUTHOR]

Cistanche deserticola Ma, a perennial parasitic herb of family Orobanchaceae, is mainly parasitic on the roots of the Haloxylon ammodendron Bunge. In view of this special parasitic relationship, we applied random forest (RF) model to forecast potential geographic distribution, and developed a comprehensive habitat suitability model by integrating bioclimatic and soil factors to assess the suitable distribution of C. deserticola and H. ammodendron across China in 2050s and 2070s under RCP2.6, RCP4.5, and RCP8.5, respectively. We modeled the core potential geographic distribution of C. deserticola by overlaying the distribution of these two species, and analyzed the spatial distribution pattern and migration trend of C. deserticola by using the standard deviational ellipse. In addition, we evaluated the accuracy of RF model through three evaluation indexes, and analyzed the dominant climate factors. The results showed that the core potential distribution areas of C. deserticola are distributed in the Xinjiang Uygur Autonomous Region, the junction of Shaanxi–Gansu–Ningxia provinces, and the Inner Mongolia Autonomous Region. The spatial dispersion would intensify with the increasing of emission scenarios, and the geographical habitat is moving towards higher latitude. Among the three evaluation indexes, the area under the ROC curve (AUC) and True Skill Statistic (TSS) have better assessment results. The main bioclimatic factors affecting the distribution are min temperature of coldest month (Bio6), annual precipitation (Bio12), precipitation of wettest month (Bio13), precipitation of wettest quarter (Bio16), and precipitation of warmest quarter (Bio18), among which the importance of precipitation factors is greater than temperature factors. More importantly, the results of this study could provide some guidance for the improvement of desert forest system, the protection of endangered species and the further improvement of the ecological environment. [ABSTRACT FROM AUTHOR]