Synthetic responses of plant and soil microbial communities to grazing are indefinite in alpine grasslands on the Tibetan Plateau. Three paired, fenced and free grazing sites (alpine steppe meadow for winter pasture [ASMWP]; alpine steppe meadow for summer pasture [ASMSP]; alpine meadow for summer pasture [AMSP]) were used to compare how pasture season and grassland type affect responses of the α‐diversity and community composition of plant, soil bacteria and fungi to grazing. Cold‐season grazing reduced soil moisture by 12.10%, ammonium nitrogen (NH4+‐N) by 53.71%, the ratio of available nitrogen to phosphorus by 64.11%, species richness (SR) by 31.4% and the Shannon by 11.9% of plant community on the ASMWP. Warm‐season grazing reduced nitrate nitrogen by 30.45%, SR of soil bacterial community by 21.98% on the ASMSP, but increased soil NH4+‐N by 90.02% on the AMSP. Warm‐season grazing‐induced changes in plant community composition were mainly related to the composition of forbs on the AMSP. Grazing‐induced changes in the community composition of soil bacteria were mainly related to Proteobacteria, Acidobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia on the ASMWP, and Proteobacteria, Acidobacteria, Bacteroidetes, Chloroflexi and TM7 on the ASMSP. Grazing‐induced changes in the community composition of soil fungi were mainly related to Ascomycota and Basidiomycota on the ASMWP, Basidiomycota on the ASMSP and Ascomycota on the AMSP. Therefore, the effects of grazing on plant and soil microbial communities may vary with grassland types and pasture seasons, which may be related to grazing‐induced changes in available nitrogen, the ratio of available nitrogen to phosphorus and soil moisture.

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.

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.