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Aim Climate change is expected to have important effects on plant phenology and carbon storage, with further shifts predicted in the future. Therefore, we proposed the community carbon accumulation rate (CAR) from the start of the growing season (SOS) to the peak of the growing season (POS) to fill the gap that the dynamic interactions between plant phenology and plant carbon research. Location Tibetan Plateau. Major taxa Alpine grassland plants. Time period 2015. Methods We conducted a transect survey across grasslands to measure community aboveground net primary production and carbon concentration. Additionally, phenology indicator data (SOS and POS) were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 database. Next, we used ‘changepoint’ analysis to detect the patterns of CARs, and performed linear regression and one‐way ANOVA to explore the variability of CARs in response to the environmental factors. Ultimately, the total effects of environmental factors on CARs were illustrated by a structural equation model. Results Our results indicated that three CAR patterns were detected, which are low‐CAR (0.15 g/m2/day), medium‐CAR (0.31 g/m2/day) and high‐CAR (0.84 g/m2/day) patterns. We found that the availabilities of water and heat mediated CARs by regulating soil nutrition variability, and that drought climate and insufficient soil resources co‐constrained the community CAR at long time‐scales. In contrast, high CAR could be explained by more water and heat availability via either direct or indirect effects on soil moisture and soil nutrients. Main conclusions Our findings highlight that water and heat availability are critical driving factors in ecological carbon accumulation processes undergoing climate change. Meanwhile, the vegetative phenology also has important effect on carbon accumulation. Consequently, we propose incorporating the dynamic interactions between plant phenology and plant carbon into the ecological carbon cycle model to improve our understanding of resource utilization and survival strategies of plants under environmental change.

Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.

Aim We aimed to explore the general response patterns of plant biomass allocation to grazing disturbance and to test two important hypotheses, optimal partitioning and isometric allocation, for explaining potential mechanisms by which grazing controls biomass distribution in an alpine grassland on the Tibetan Plateau. Methods We identified 57 relevant papers about grazing on the Tibetan Plateau, from which 366 data sets suitable for the meta-analysis were extracted. Effect sizes were assessed by computing natural log-converted response ratios of response variables. Percentage change relative to control was used for each estimate of grazing effects. Results The aboveground biomass, soil water content (SWC), soil organic carbon, soil total nitrogen, and soil total phosphorus significantly decreased with increased grazing intensities, while plant species richness (SR), soil bulk density (SBD) and the ratio of root to shoot exhibited the opposite tendency. Belowground biomass (BGB) showed no significant differences under light and high grazing intensities while apparently increased under moderate grazing intensity (MG) that verifies the biomass transfer hypothesis. BGB was positively related to SBD and SR but was negatively associated with SWC. Conclusions The biomass transfer in MG supports the optimal partitioning hypothesis that plants partition biomass among various organs to maximize growth rate responding to environmental stress. The findings suggest that the primary mechanisms leading to the enhancement of BGB in MG are compensatory growth of individual plants, a dwarfing tendency within the plant community, a significant increase in species richness, and changes in soil microbial communities resulting from grazing.

Understanding the importance of temperature and precipitation on plant productivity is beneficial, to reveal the potential impact of climate change on vegetation growth. Although some studies have quantified the response of vegetation productivity to climate change at local, regional, and global scales, changes in climatic constraints on vegetation productivity over time are not well understood. This study combines the normalized difference vegetation index (NDVI) and the net primary production (NPP) modeled by CASA during the plant-growing season, to quantify the interplay of climatic (growing-season temperature and precipitation, GST and GSP) constraints on alpine-grassland productivity on the Tibetan Plateau, as well as the temporal dynamics of these constraints. The results showed that (1) 42.2% and 36.3% of grassland NDVI and NPP on the Tibetan Plateau increased significantly from 2000 to 2019. GSP controlled grassland growth in dryland regions, while humid grasslands were controlled by the GST. (2) The response strength of the NDVI and NPP to precipitation (partial correlation coefficient RNDVI-GSP and RNPP-GSP) increased substantially between 2000 and 2019. Especially, the RNDVI-GSP and RNPP-GSP increased from 0.14 and 0.01 in the first 10year period (2000–2009) to 0.83 and 0.78 in the second 10-year period (2010–2019), respectively. As a result, the controlling factor for alpine-grassland productivity variations shifted from temperature during 2000–2009 to precipitation during 2010–2019. (3) The increase in precipitation constraints was mainly distributed in dryland regions of the plateau. This study highlights that the climatic constraints on alpine-grassland productivity might change under ongoing climate change, which helps the understanding of the ecological responses and helps predict how vegetation productivity changes in the future.

Alpine plants in nitrogen-deficient environments can acquire nitrogen by associating with endophytic nitrogen-fixing microorganisms that inhabit their roots and leaves to form symbiotic relationships. However, research is limited on nitrogen-fixing bacterial communities in the roots and leaves of alpine grassland plants, especially regarding the differences between various plant parts. In this study, we compared the root and leaf bacterial communities of four alpine plant families (Asteraceae, Leguminosae, Poaceae, and Rosaceae) in the alpine meadow ecosystem of Naqu, Tibet, using culture-based methods, 16S rRNA, and nifH gene pyrosequencing. The results showed greater bacterial diversity in the root compared to the leaf, and Fabaceae plants harbored a higher abundance of nitrogen-fixing bacteria. Interestingly, the roots and leaves of non-Fabaceae plants (Kobresia, Festuca ovina, and Leontopodium) also harbored abundant nitrogen-fixing communities such as Microbacterium, Curtobacterium, and Rhodococcus. Compared with subtropical environments, Cyanobacteria are important symbiotic nitrogen-fixing bacteria in plants of alpine ecosystems. These findings indicate that plant species and plant parts strongly influence the selection of bacterial populations. Understanding these microbial ecological functions in alpine grasslands provides scientific insights for optimizing agricultural practices and ecosystem management.

Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of high-precision FVC ground-measured data, the accuracy of these FVC products in some regions (such as the Qinghai–Tibet Plateau) is still unknown, which brings a certain impact on eco-environment monitoring and simulation. Here, based on current international mainstream FVC products (including GEOV1 and GEOV2 at Copernicus Global Land Services, GLASS from Beijing Normal University, and MuSyQ from National Earth System Science Data Center), the study of the dynamic change of vegetation cover and its influence factors were conducted in the three-rivers source region, one of the core regions on the Qinghai–Tibet Plateau, via the methods of trend analysis and partial correlation analysis, respectively. Our results found that: (1) The discrepancy in the eco-environment assessment results caused by the inconsistency of FVC products is reflected in the statistical value and the spatial distribution. (2) About 70% of alpine grassland in the three-rivers source region changing trend is controversial. (3) The limiting or driving factors of the alpine grassland change explained via different FVC products were significantly discrepant. Thus, before conducting these studies in the future, the uncertainties of the FVC products utilized should be validated first to acquire the fitness of the FVC products if the accuracy information of these products is unavailable within the study area. In addition, more high-precision FVC ground-measured data should be collected, helping us to validate FVC product uncertainty.

Grassland ecosystems are affected by the increasing frequency and intensity of extreme climate events (e.g., droughts). Understanding how grassland ecosystems maintain their functioning, resistance, and resilience under climatic perturbations is a topic of current concern. Resistance is the capacity of an ecosystem to withstand change against extreme climate, while resilience is the ability of an ecosystem to return to its original state after a perturbation. Using the growing season Normalized Difference Vegetation Index (NDVI gs , an index of vegetation growth) and the Standardized Precipitation Evapotranspiration Index (a drought index), we evaluated the response, resistance, and resilience of vegetation to climatic conditions for alpine grassland, grass-dominated steppe, hay meadow, arid steppe, and semi-arid steppe in northern China for the period 1982–2012. The results show that NDVI gs varied significantly across these grasslands, with the highest (lowest) NDVI gs values in alpine grassland (semi-arid steppe). We found increasing trends of greenness in alpine grassland, grass-dominated steppe, and hay meadow, while there were no detectable changes of NDVI gs in arid and semi-arid steppes. NDVI gs decreased with increasing dryness from extreme wet to extreme dry. Alpine and steppe grasslands exhibited higher resistance to and lower resilience after extreme wet, while lower resistance to and higher resilience after extreme dry conditions. No significant differences in resistance and resilience of hay meadow under climatic conditions suggest the stability of this grassland under climatic perturbations. This study concludes that highly resistant grasslands under conditions of water surplus are low resilient, but low resistant ecosystems under conditions of water shortage are highly resilient.

The interrelation between grassland vegetation greenness and hydrothermal conditions on the Tibetan Plateau demonstrates a significant correlation. However, understanding the spatial patterns and the degree of this correlation, especially in relation to minimum and maximum air temperatures across various vertical gradient zones of the Plateau, necessitates further examination. Utilizing the normalized difference phenology index (NDPI) and considering four distinct hydrothermal conditions (minimum, maximum, mean temperature, and precipitation) during the growing season, an analysis was conducted on the correlation of NDPI with hydrothermal conditions across plateau elevations from 2000 to 2021. Results indicate that the correlation between vegetation greenness and hydrothermal conditions on the Tibetan Plateau grasslands is spatially varied. There is a pronounced negative correlation of greenness to maximum temperature and precipitation in the northeastern plateau, while areas exhibit stronger positive correlations to mean temperature. Additionally, as elevation increases, the positive correlation and sensitivity of alpine grassland vegetation greenness to minimum temperature significantly intensify, contrary to the effects observed with maximum temperature. The correlations between greenness and mean temperature in relation to elevational changes primarily exhibit a unimodal pattern across the Tibetan Plateau. These findings emphasize that the correlation and sensitivity of grassland vegetation greenness to hydrothermal conditions are both elevation-dependent and spatially distinct.

Alpine grassland soils accumulate massive stocks of organic carbon and function as important carbon sinks on the Qinghai-Tibetan Plateau. Substantial uncertainties prevent a full understanding of ecosystems’ carbon pools and their responses to environmental factors, mainly because of limited observations and inconsistent up-scaling algorithms. This study compiled data since 2000 for 422 alpine grassland sites in Qinghai Province, China, to investigate vegetation and organic carbon densities in the topsoil (at 0–30 cm depth) and their spatial variations. The site-averaged below-ground biomass carbon density (BOD) and topsoil organic carbon density (SOD) were both highest in alpine meadows with values of 0.43 ± 0.34 (Mean ± S.D.) and 12.52 ± 5.74 kg C/m2, respectively. They are about five times the lowest corresponding values of alpine desert steppes. The above-ground biomass carbon density (AOD) is not significantly different between alpine steppes and alpine desert steppes, and averaged 32.66 ± 22.02 g C/m2, around twice that for alpine meadows. Boosted regression tree models and a structural equation model consistently show that mean annual precipitation, rather than mean annual air temperature, was the predominant factor influencing the spatial variability of site-level AOD, BOD, and SOD across the alpine grasslands. The boosted regression tree models, integrated with spatial datasets of topographic attributes, mean annual air temperature and precipitation, and mean annual maximal normalized difference vegetation index, yielded area-averaged AOD, BOD, and SOD values of 22.67 ± 4.48 g C/m2, 0.37 ± 0.074 kg C/m2, and 9.53 ± 4.48 kg C/m2, respectively. Modeling results indicate that ecosystem carbon densities increase from northwest to southeast, mainly following the spatial patterns of vegetation greenness and precipitation. The size of the total terrestrial ecosystem carbon pool in Qinghai province is estimated to be 3.65 Pg C, of which 96.02% is stored in topsoil, 3.75% in below-ground biomass, and 0.23% in above-ground biomass. Alpine meadows and alpine steppes account for 70.9% and 15.6% of the total carbon stocks of Qinghai province, respectively. Our results provide improved estimates of carbon pools and underscore the predominance of precipitation in determining spatial variability in the carbon stocks of alpine grassland ecosystems. Our findings will help elucidate the feedback mechanisms between carbon storage and climate changes in alpine grassland.

AimsLitter decomposition is a crucial component of nutrient recycling. Short-term nitrogen (N) deposition has been shown to influence litter decomposition in temperate steppe with significant variability due to differences in atmospheric N deposition, species identity, and experimental duration. Therefore, the effect of N addition, especially long-term, on litter decomposition in alpine grassland still needs further investigation.MethodsTo address these knowledge gaps, we examined the influence of long-term N addition on litter decomposition, taking advantage of a field experiment with five N addition levels (0, 10, 30, 90, and 150 kg N ha−1 yr−1) with a meta-analysis, which has been running for 11 years in an alpine grassland, Northwest China.ResultsLong-term N addition consistently decreased litter decomposition rates, and N negative effect became stronger with the increasing N addition rates. Reduced litter decomposition rates were related to lower soil enzymes activities. Litter decomposition rates were strongly correlated with litter quality, but weakly correlated with soil quality, but which suggested that litter quality and soil quality played important role in regulating litter decomposition. Furthermore, a regional meta-analysis revealed that N addition accelerated litter decomposition when all data were averaged. Although N addition indirectly increased litter decomposition, it had no direct effect on decomposition. However, the direction and degree of the direct effect of N on litter decomposition were regulated by N addition rate, experimental duration and form of N fertilizer.ConclusionsOverall, these results demonstrated that long-term N addition decreased litter decomposition and N negative effect increased over time.