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Many studies have investigated the effects of environmental context on biodiversity or multifunctionality in alpine regions, but it is uncertain how human pressure and climate may affect their relationships. Here, we combined the comparative map profile method with multivariate datasets to assess the spatial pattern of ecosystem multifunctionality and further identify the effects of human pressure and climate on the spatial distribution of biodiversity–multifunctionality relationships in alpine ecosystems of the Qinghai–Tibetan Plateau (QTP). Our results indicate that at least 93% of the areas in the study region show a positive correlation between biodiversity and ecosystem multifunctionality across the QTP. Biodiversity–multifunctionality relationships with increasing human pressure show a decreasing trend in the forest, alpine meadow, and alpine steppe ecosystems, while an opposite pattern was found in the alpine desert steppe ecosystem. More importantly, aridity significantly strengthened the synergistic relationship between biodiversity and ecosystem multifunctionality in forest and alpine meadow ecosystems. Taken together, our results provide insights into the importance of protecting and maintaining biodiversity and ecosystem multifunctionality in response to climate change and human pressure in the alpine region.

Increasing aridity and grazing have multifaceted effects on the ecosystem, of which plant traits interact with each other to coregulate the productivity function including above- and below- ground compartments of plant. The ecological interaction network among plant traits could reflect how ecosystem interact with, influence, and respond to environment change. In this study, the interaction network between leaf, root, and biomass function was revealed to indicate systemic ecosystem response to increasing aridity and grazing by sampling in enclosed plots along the aridity gradient and in neighboring grazing plots of China drylands. Systemic and abrupt changes in the network relationship between leaf, root, and biomass were observed with a threshold value of 1-AI (aridity index) ∼ 0.8. The control effect of roots on other traits and the interaction of leaf and root compartments increased in the opposite direction above the threshold. The change in the relative importance of key traits with increasing aridity caused the different grazing effects. Grazing decreased the leaf area in the area with1-AI ∼ 0.6, whereas it increased the effect of roots in the area with 1-AI ∼ 0.8, thus promoting the interaction of functional traits. This result suggests the essential role of the interaction network between plant traits in ecosystem response to environmental change, which could indicate the ecosystem change in a holistic perspective under global change.

Abstract Objective To analyze the differential expression of autophagy‐related genes of sepsis‐induced acute respiratory distress syndrome (ARDS) as potential markers for early diagnosis. Methods Male Sprague–Dawley rats (aged 8 weeks) were selected and randomly divided into sepsis‐induced ARDS group (n = 6) and a normal control group (n = 6). Lung tissue samples were collected for high‐throughput sequencing using Illumina HiSeq sequencing platform in the paired‐end sequencing mode. Differentially expressed genes (DEGs) were screened by DESeq. 2 software [|log2FC | ≥1 and p

The potential patterns and processes of ecosystem multifunctionality (EMF) across global ecosystems are largely unknown, which limits our understanding of how ecosystems respond to drivers. Here we compile a global dataset that consists of 973 unique sites across the forest, grassland, and shrub ecosystems. We identify a critical global pattern of hump-shaped EMF relationship with mean annual precipitation at a threshold of ∼671 mm, where low and high precipitation patterns are discriminated. We find that climatic and soil factors jointly drive the EMF in low precipitation areas, and climatic factors dominate the EMF in high precipitation regions. However, when comparing across the three dominant ecosystems and precipitation regions, the key driver in EMF differs substantially. Specifically, climatic and soil factors dominate the EMF of low and high precipitation regions across forest ecosystems, respectively. Climatic drivers dominate the EMF under different precipitation conditions across grassland and shrub ecosystems. Overall, our findings highlight the importance of climatic and soil drivers on EMF, which should be considered in ecosystem stability models in response to global climate and land-use change scenarios.

The multiple functions and services provided by ecosystems that linked human-being are under threat with the global environment changes. Large-scale research of ecosystem multifunctionality (EMF), which include AN, AP, AK, CEC, SOM, SMC, SMN, AGB BGB and other indicators, can help us excavate ecosystems more comprehensively and strengthen protection awareness of biodiversity, but it remains scarce, especially for the Qinghai-Tibet Plateau (QTP). In this paper, evaluating EMF by the three calculation approaches–average approach, single threshold approach, and principal component analysis, we presented the spatial pattern of EMF over the QTP. We found that three approaches undergone the coincident spatial variation characteristic, with a decreasing gradient from southeast to northwest. Although the EMFs of forests (geometric mean of three indexes = 0.37) and alpine meadows (geometric mean of three indexes = 0.29) were significantly higher than those of alpine steppe (geometric mean of three indexes = 0.16) and alpine desert steppe (geometric mean of three indexes = 0.12) ecosystems, the EMFs jointly exhibited an inverted U-shaped distribution with altitudinal gradient in all ecosystems. For the correlation between EMF and precipitation, which first increases and then decreases along altitudinal gradient, with a turnaround around 4000–5000 m. Nevertheless, the correlation between EMF and temperature, which universally increases with altitudinal gradient. Our results emphasize that gradient change of EMF is attributed to elevation dependence of climate change throughout the QTP, and understanding transformation threshold that is beneficial for the protection and sustainable development of ecosystems.

Net primary productivity (NPP) is an important indicator of ecosystem function and sustainability. It plays an important role in the global carbon cycle, especially in the Tibetan Plateau (TP). Until now, few studies have explored the relationships between climate change, phenology, edaphic factors, and NPP. Quantifying NPP trends and exploring the response mechanisms of NPP to climate change, phenology, and edaphic factors is of great significance to ecosystem maintenance and related policy-making. In this study, the length of the growing season (LOS) was retrieved using the threshold method, which was based on normalized difference vegetation index (NDVI) data. Meanwhile, the Carnegie-Ames-Stanford approach model was adopted to develop an NPP dataset based on multiple datasets across the TP from 2000 to 2020. Then, the availability of NPP and LOS datasets were evaluated. On this basis, this study investigated the spatiotemporal patterns of the NPP, and quantitatively analyzed the response mechanism of NPP to climate change, phenology, and edaphic factors in different ecosystems. According to our results, the estimated NPP and LOS datasets had good usability and robustness. The annual total NPP in the TP experienced a significant upward trend (with Slope of 0.88 gCm-2year−1, R2 = 0.42, P 850 gC/m2). In addition, the response mechanism of NPP to climate change, phenology, and edaphic factors indicated that the effects of climate change, phenology, and edaphic factors on NPP varied significantly with different terrestrial ecosystems. Specifically, the availabilities of water and heat mediated NPP by regulating soil moisture variability in the alpine meadow (standardized total effect coefficient (STEC) = 0.58) and alpine steppe (STEC = 0.47), but regulating soil temperature (STEC = 0.29) and LOS (STEC = 0.31) in forest. Our results highlighted that soil properties characteristics of the TP should be considered for combating future climate change.

The distribution of alpine vegetation is highly sensitive to climate change, which attracted the attention of climate scientists as well as ecologists. However, the dominant factors of vegetation distribution showed greatly spatiotemporal variation, especially the alpine grasslands on the Tibetan Plateau, which is known as an amplifier of climate warming. In this study, to identify the dominant factors of vegetation distribution, we verified the reliability and accuracy of the classification of alpine steppe and alpine meadow by Random Forest and tried to provide a new reference for the classification schemes. Dataset collected from the field investigations (200 sites) and previous publications(200 sites) were used in this study for model calibration and validation. Then climate models of CMIP6 were used to forecast the underlying transfers and changes of vegetation boundary in the future. Our results revealed that precipitation may be the dominant driver in the alpine grassland distribution, with the highest relative importance of 41.26%. Through different climate scenarios from 2000 to 2100, the vegetation boundary showed a shift from northeast to southwest. Within the SSP5-8.5 scenario it moves from 94.50° to 93.49° and shifted from north to south by 0.34°. Our results demonstrated the significant role of precipitation in the alpine grassland distribution and revealed the migration of the alpine grassland ecosystem under different climate change scenarios. Our work may be useful to deal with global climate change, early warning, and grassland protection, as well as adaptive management of grassland.

Abstract The sharp rise in anthropogenic activities and climate change has caused the extensive degradation of grasslands worldwide, jeopardizing ecosystem function, and threatening human well‐being. Toxic weeds have been constantly spreading in recent decades; indeed, their occurrence is considered to provide an early sign of land degeneration. Policymakers and scientific researchers often focus on the negative effects of toxic weeds, such as how they inhibit forage growth, kill livestock, and cause economic losses. However, toxic weeds can have several potentially positive ecological impacts on grasslands, such as promoting soil and water conservation, improving nutrient cycling and biodiversity conservation, and protecting pastures from excessive damage by livestock. We reviewed the literature to detail the adaptive mechanisms underlying toxic weeds and to provide new insight into their roles in degraded grassland ecosystems. The findings highlight that the establishment of toxic weeds may provide a self‐protective strategy of degenerated pastures that do not require special interventions. Consequently, policymakers, managers, and other personnel responsible for managing grasslands need to take appropriate actions to assess the long‐term trade‐offs between the development of animal husbandry and the maintenance of ecological services provided by grasslands.

Satellite observations since the early 1980s have revealed a trend of ‘Earth greening’ across global terrestrial ecosystems. Dryland vegetation is more sensitive to climate change and human activities. China’s drylands are among the largest in extent worldwide, and large-scale ecological restoration of these areas has been implemented since the late 1970s, which has resulted in more complicated but still poorly quantified vegetation dynamics. To figure out the vegetation dynamics and associated driving forces, we provide an assessment of the vegetation dynamics from 1982 to 2015 using the CO _2 fertilization effect function, principal component regression, Residual Trend analysis, and Breaks For Additive Seasonal and Trend methods based on the ERA5 climate factors and GIMMS 3.1 normalized difference vegetation index datasets. This study shows that anthropogenic impacts and CO _2 fertilization have jointly led to vegetation greening in China’s drylands since the 1980s, and ecological restoration has accelerated this greening since the 2000s. The results show that the vegetation greening in China’s drylands (41.51% of the study area, +0.60 × 10 ^−3 yr ^−1 ) is mainly driven by CO _2 fertilization (+0.55 × 10 ^−3 yr ^−1 ) and anthropogenic activities (+0.12 × 10 ^−3 yr ^−1 ). The anthropogenic effects are especially higher on the Loess Plateau (+1.01 × 10 ^−3 yr ^−1 ) and the Three-North region (+0.23 × 10 ^−3 yr ^−1 ). The vegetation dynamics shifts in 6.73% (31.64 Mha) of China’s drylands were directly attributed to anthropogenic impacts around the 2000s. When the anthropogenic effect was intensified, the vegetation dynamics shifted from no change to greening and vice versa, which significantly intensified the vegetation greening since the 1980s. These results capture the processes of ecological programs and provide an assessment of the effects of ecological restoration. This work provides a credible attribution of the vegetation greenness dynamics and trend shifts in China’s drylands, thus facilitating a better understanding of regional environmental change and management.

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R2 = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R2 = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development.

Variations in surface physicochemical properties and spatial structures can prominently transform surface albedo which conversely influence surface energy balances and global climate, making it crucial to continuously monitor and quantify surface dynamics at fine scales. Here, we made two improvements to propose an algorithm for the simultaneous retrieval of 30-m Landsat albedo, based on the coupling of Landsat-8 and MODIS BRDF. First, two kinds of prior knowledge were added to disaggregate BRDF, including the Anisotropic Flat Index (AFX) and the Albedo-to-Nadir reflectance ratio (AN ratio), from MODIS scales into Landsat scales. Second, a simplified data fusion method was used to simulate albedo for the same, subsequent, or antecedent dates. Finally, we validated the reliability and correlations of the algorithm at six sites of the Surface Radiation (SURFRAD) budget network and intercompared the results with another algorithm called the ‘concurrent approach’. The results showed that the proposed algorithm had favorable usability and robustness, with a root mean square error (RMSE) of 0.015 (8%) and a mean bias of −0.005; while the concurrent approach had a RMSE of 0.026 (14%) and a mean bias of −0.018. The results emphasized that the proposed algorithm has captured subtle changes in albedo over a 16-day period.

Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management.

The alpine timberline, an ecosystem ecotone, indicates climatic change and is tending to shift toward higher altitudes because of an increase in global warming. However, spatiotemporal variations of the alpine timberline are not consistent on a global scale. The abundant and highest alpine timberline, located on the Tibetan Plateau, is less subject to human activity and disturbance. Although many studies have investigated the alpine timberline on the Tibetan Plateau, large-scale monitoring of spatial-temporal dynamics and driving mechanisms of the alpine timberline remain uncertain and inaccurate. Hence, the Gongga Mountain on the southeastern Tibetan Plateau was chosen as the study area because of the most complete natural altitudinal zonation. We used the Otsu method on Google Earth Engine to extract the alpine timberline from 1987–2019 based on the normalized difference vegetation index (NDVI). Then, the alpine timberline spatiotemporal patterns and the effect of topography on alpine timberline distribution were explored. Four hillsides on the western Gongga Mountain were selected to examine the hillside differences and drivers of the alpine timberline based on principal component analysis (PCA) and multiple linear regression (MLR). The results indicated that the elevation range of alpine timberline was 3203–4889 m, and the vegetation coverage increased significantly (p < 0.01) near the alpine timberline ecotone on Gongga Mountain. Moreover, there was spatial heterogeneity in dynamics of alpine timberline, and some regions showed no regular trend in variations. The spatial pattern of the alpine timberline was generally high in the west, low in the east, and primarily distributed on 15–55° slopes. Besides, the drivers of the alpine timberline have the hillside differences, and the sunny and shady slopes possessed different driving factors. Thus, our results highlight the effects of topography and climate on the alpine timberline on different hillsides. These findings could provide a better approach to study the dynamics and formation of alpine timberlines.

Climatic extremes have adverse concurrent and lagged effects on terrestrial carbon cycles. Here, a concurrent effect refers to the occurrence of a latent impact during climate extremes, and a lagged effect appears sometime thereafter. Nevertheless, the uncertainties of these extreme drought effects on net carbon uptake and the recovery processes of vegetation in different Tibetan Plateau (TP) ecosystems are poorly understood. In this study, we calculated the Standardised Precipitation–Evapotranspiration Index (SPEI) based on meteorological datasets with an improved spatial resolution, and we adopted the Carnegie–Ames–Stanford approach model to develop a net primary production (NPP) dataset based on multiple datasets across the TP during 1982–2015. On this basis, we quantised the net reduction in vegetation carbon uptake (NRVCU) on the TP, investigated the spatiotemporal variability of the NPP, NRVCU and SPEI, and analysed the NRVCUs that are caused by the concurrent and lagged effects of extreme drought and the recovery times in different ecosystems. According to our results, the Qaidam Basin and most forest regions possessed a significant trend towards drought during 1982–2015 (with Slope of SPEI < 0, P < 0.05), and the highest frequency of extreme drought events was principally distributed in the Qaidam Basin, with three to six events. The annual total net reduction in vegetation carbon uptake on the TP experienced a significant downward trend from 1982 to 2015 (−0.0018 ± 0.0002 PgC year−1, P < 0.001), which was negatively correlated with annual total precipitation and annual mean temperature (P < 0.05). In spatial scale, the NRVCU decrement was widely spread (approximately 55% of grids) with 17.86% of the area displaying significant declining trends (P < 0.05), and the sharpest declining trend (Slope ≤ −2) was mainly concentrated in southeastern TP. For the alpine steppe and alpine meadow ecosystems, the concurrent and lagged effects of extreme drought induced a significant difference in NRVCU (P < 0.05), while forests presented the opposite results. The recovery time comparisons from extreme drought suggest that forests require more time (27.62% of grids ≥ 6 years) to recover their net carbon uptakes compared to grasslands. Therefore, our results emphasise that extreme drought events have stronger lagged effects on forests than on grasslands on the TP. The improved resilience of forests in coping with extreme drought should also be considered in future research.

Flash floods are one of the most destructive natural disasters. The comprehensive identification of the spatiotemporal characteristics and driving factors of a flash flood is the basis for the scientific understanding of the formation mechanism and the distribution characteristics of flash floods. In this study, we explored the spatiotemporal patterns of flash floods in Fujian Province from 1951 to 2015. Then, we analyzed the driving forces of flash floods in geomorphic regions with three different grades based on three methods, namely, geographical detector, principal component analysis, and multiple linear regression. Finally, the sensitivity of flash floods to the gross domestic product, village point density, annual maximum one-day precipitation (Rx1day), and annual total precipitation from days > 95th percentile (R95p) was analyzed. The analytical results indicated that (1) The counts of flash floods rose sharply from 1988, and the spatial distribution of flash floods mainly extended from the coastal low mountains, hills, and plain regions of Fujian (IIA2) to the low-middle mountains, hills, and valley regions in the Wuyi mountains (IIA4) from 1951 to 2015. (2) From IIA2 to IIA4, the impact of human activities on flash floods was gradually weakened, while the contribution of precipitation indicators gradually strengthened. (3) The sensitivity analysis results revealed that the hazard factors of flash floods in different periods and regions had significant differences in Fujian Province. Based on the above results, it is necessary to accurately forecast extreme precipitation and improve the economic development model of the IIA2 region.

Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.

The Tibetan Plateau is one of the most vulnerable areas to extreme precipitation. In recent decades, water cycles have accelerated, and the temporal and spatial characteristics of extreme precipitation have undergone dramatic changes across the Tibetan Plateau, especially in its various ecosystems. However, there are few studies that considered the variation of extreme precipitation in various ecosystems, and the impact of El Niño-Southern Oscillation (ENSO), and few researchers have made a quantitative analysis between them. In this study, we analyzed the spatial and temporal pattern of 10 extreme precipitation indices across the Tibetan Plateau (including its four main ecosystems: Forest, alpine meadow, alpine steppe, and desert steppe) based on daily precipitation from 76 meteorological stations over the past 30 years. We used the linear least squares method and Pearson correlation coefficient to examine variation magnitudes of 10 extreme precipitation indices and correlation. Temporal pattern indicated that consecutive wet days (CWD) had a slightly decreasing trend (slope = −0.006), consecutive dry days (CDD), simple daily intensity (SDII), and extreme wet day precipitation (R99) displayed significant increasing trends, while the trends of other indices were not significant. For spatial patterns, the increasing trends of nine extreme precipitation indices (excluding CDD) occurred in the southwestern, middle and northern regions of the Tibetan Plateau; decreasing trends were distributed in the southeastern region, while the spatial pattern of CDD showed the opposite distribution. As to the four different ecosystems, the number of moderate precipitation days (R10mm), number of heavy precipitation days (R20mm), wet day precipitation (PRCPTOT), and very wet day precipitation (R95) in forest ecosystems showed decreasing trends, but CDD exhibited a significant increasing trend (slope = 0.625, P < 0.05). In the other three ecosystems, all extreme precipitation indices generally exhibited increasing trends, except for CWD in alpine meadow (slope = −0.001) and desert steppe (slope = −0.005). Furthermore, the crossover wavelet transform indicated that the ENSO had a 4-year resonance cycle with R95, SDII, R20mm, and CWD. These results provided additional evidence that ENSO play an important remote driver for extreme precipitation variation in the Tibetan Plateau.

Grassland degradation in Altay Prefecture is of considerable concern as it is a threat that hinders the sustainable development of the local economy and the stable operation of the livestock industry. Quantitative assessment of the relative contributions of climate change and human activities, which are considered as the dominant triggers of grassland degradation, to grassland variation is crucial for understanding the grassland degradation mechanism and mitigating the degraded grassland in Altay Prefecture. In this paper, the Carnegie-Ames-Stanford Approach model and the Thornthwaite memorial model were adopted to simulate the actual net primary productivity (NPPA) and potential net primary productivity (NPPP) in the Altay Prefecture from 2000 to 2019. Meanwhile, the difference between potential NPP and actual NPP was employed to reflect the effects of human activities (NPPH) on the grassland. On this basis, we validated the viability of the simulated NPP using the Pearson correlation coefficient, investigated the spatiotemporal variability of grassland productivity, and established comprehensive scenarios to quantitatively assess the relative roles of climate change and human activities on grassland in Altay prefecture. The results indicate three main points. (1) The simulated NPPA was highly consistent with the MOD17A3 dataset in spatial distribution. (2) Regions with an increased NPPA accounted for 70.53% of the total grassland, whereas 29.47% of the total grassland area experienced a decrease. At the temporal scale, the NPPA presented a slightly increasing trend (0.83 g C m–2 yr–1) over the study period, while the trends of NPPP and NPPH were reduced (–1.31 and –2.15 g C m–2 yr–1). (3) Compared with climate change, human activities played a key role in the process of grassland restoration, as 66.98% of restored grassland resulted from it. In contrast, inter-annual climate change is the primary cause of grassland degradation, as it influenced 55.70% of degraded grassland. These results could shed light on the mechanisms of grassland variation caused by climate change and human activities, and they can be applied to further develop efficient measures to combat desertification in Altay Prefecture. [ABSTRACT FROM AUTHOR]

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture. [ABSTRACT FROM AUTHOR]