Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m⁻², respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet.

Quantifying the impact of climate change and vegetation phenology on ecosystem productivity in the alpine grasslands of the Tibetan Plateau (TP) is essential for assessing carbon balance dynamics at regional and global scales. However, the relative contributions of climate change and phenological change to vegetation productivity across various grassland types remain indistinguishable. This study examined the effects of climate change and phenological change on net primary productivity (NPP) in the alpine meadow and alpine steppe ecosystems of the TP from 2001 to 2020. The results revealed that (1) NPP exhibited a positive correlation with vegetation phenology, particularly with an extended growing season length and an earlier start of the growing season. Among the phenological variables studied, changes in the start of the growing season had the strongest influence on NPP variations in both alpine meadows and alpine steppes. (2) NPP displayed a positive correlation with annual precipitation and annual temperature, with changes in annual precipitation playing a dominant role in shaping NPP variations in alpine steppes. (3) NPP showed a negative correlation with annual radiation, and the impact of radiation changes on NPP variations was comparable to that of precipitation or temperature in both alpine meadows and alpine steppes. (4) Climate change exerted a stronger impact on NPP than phenological change in alpine steppes, while NPP was jointly affected by climate change and phenological change in alpine meadows. Our findings indicated that the relative effects of climate change and phenological change on vegetation productivity vary across different grassland types on the TP.

Ecological stability contains multiple components, such as temporal invariability, resistance and resilience. Understanding the response of stability components to perturbations is beneficial for optimizing the management of biodiversity and ecosystem functioning. Although previous studies have investigated the effects of multiple perturbations on each stability component, few studies simultaneously measure the multiple stability components and their relationships. Alpine grasslands on the Tibetan Plateau are exposed to co-occurring perturbations, including climate change and human activities. Here, we quantified three stability components (temporal invariability, resistance, and resilience) of alpine grasslands on the Tibetan Plateau during periods of high (2000-2008) and low (2009-2017) human activity intensity, respectively. We focused on the effects of climate variables (temperature, precipitation, radiation) and human activities (grazing intensity) on covariation among stability components. The results show that (1) for periods of high and low human activity, temporal invariability was positively correlated with resistance and resilience, while resistance was independent of resilience; (2) the dimensionality of alpine grasslands decreased by almost 10%, from 0.61 in the first period to 0.55 in the second period, suggesting the increasing connections among temporal invariability, resistance and resilience of alpine grasslands; and (3) temperature but not grazing intensity dominated the changes in the dimensionality of stability. These findings improve our understanding of multi-dimensional stability and highlight the importance of climate variability on alpine grassland stability on the Tibetan Plateau.

There is still a lack of high-precision and large-scale soil ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N) and available phosphorus (AP) in alpine grasslands at least on the Qinghai–Xizang Plateau, which may limit our understanding of the sustainability of alpine grassland ecosystems (e.g., changes in soil NH₄⁺-N, NO₃⁻-N and AP can affect the sustainability of grassland productivity, which in turn may alter the sustainability of livestock development), given that nitrogen and phosphorus are important limiting factors in alpine regions. The construction of big data mining models is the key to solving the problem mentioned above. Therefore, observed soil NH₄⁺-N, NO₃⁻-N and AP at 0–10 cm and 10–20 cm, climate data (air temperature, precipitation and radiation) and/or normalized vegetation index (NDVI) data were used to model NH₄⁺-N, NO₃⁻-N and AP in alpine grasslands of Xizang under fencing and grazing conditions. Nine algorithms, including random forest algorithm (RFA), generalized boosted regression algorithm (GBRA), multiple linear regression algorithm (MLRA), support vector machine algorithm (SVMA), recursive regression tree algorithm (RRTA), artificial neural network algorithm (ANNA), generalized linear regression algorithm (GLMA), conditional inference tree algorithm (CITA), and eXtreme gradient boosting algorithm (eXGBA), were used. The RFA had the best performance among the nine algorithms. Climate data based on the RFA can explain 78–92% variation of NH₄⁺-N, NO₃⁻-N and AP under fencing conditions. Climate data and NDVI together can explain 83–93% variation of NH₄⁺-N, NO₃⁻-N and AP under grazing conditions based on the RFA. The absolute values of relative bias, linear slopes, R² and RMSE values between simulated soil NH₄⁺-N, NO₃⁻-N and AP based on RFA were ≤8.65%, ≥0.90, ≥0.91 and ≤3.37 mg kg⁻¹, respectively. Therefore, random forest algorithm can be used to model soil available nitrogen and phosphorus based on observed climate data and/or normalized difference vegetation index in Xizang’s grasslands. The random forest models constructed in this study can be used to obtain a long-term (e.g., 2000–2020) raster dataset of soil available nitrogen and phosphorus in alpine grasslands on the whole Qinghai–Tibet Plateau. The raster dataset can explain changes in grassland productivity from the perspective of nitrogen and phosphorus constraints across the Tibetan grasslands, which can provide an important basis for the sustainable development of grassland ecosystem itself and animal husbandry on the Tibetan Plateau.

In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on the climate-sensitive Tibetan Plateau, accurate AGB monitoring is crucial for assessing large-scale grassland livestock capacity. Previous studies focused on predicting AGB mainly at the plant community level and from the perspective of dry weight (AGB_d). This study aims to predict grassland AGB in Xizang at both the plant taxonomic group (sedge, graminoid, forb) and community levels, from both an AGB_d and a fresh weight (AGB_f) perspective. Three to four independent variables (growing mean temperature, total precipitation, total radiation and NDVI_max, maximum normalized difference vegetation index) were used for AGB prediction using nine models in Xizang grasslands. The random forest model (RFM) showed the greatest potential in simulating AGB (training R² ≥ 0.62, validation R² ≥ 0.87). This could be due to the nonlinear relationships between AGB, meteorological factors, and NDVI_max. The RFM exhibited robustness against outliers and zero values resulting from taxonomic groups that were absent from the quadrats. The accuracies of the RFM were different between fresh and dry weight, and among the three taxonomic groups. The RFM’s use of fewer variables can reduce complexity and costs compared to previous studies. Therefore, the RFM emerged as the optimal model among the nine models, offering potential for large-scale investigations into grassland AGB, especially for analyzing spatiotemporal patterns of plant taxonomic groups.