Asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production of alpine grasslands

It is well known that asymmetric warming among elevations (i.e., warming magnitude increases with increasing elevation) will weaken the difference of air temperature among elevations. However, it remains controversial on whether asymmetric warming among elevations can homogenize plant α-diversity and above-ground net primary production (ANPP) in alpine regions. In the present study, we conducted an experiment of asymmetric warming among elevations in alpine grasslands, Northern Tibet since 2010. There were four experiment treatments, including a treatment under natural conditions at elevation 4,313 m (C4313), a treatment under natural conditions at elevation 4,513 m (C4513), a treatment under warming conditions at elevation 4,513 m (W4513) and a treatment under warming conditions at elevation 4,693 m (W4693). We investigated ANPP, taxonomic α-diversity (i.e., species richness, Shannon, Simpson and Pielou) and phylogenetic α-diversity (mean nearest taxon distance, MNTD; phylogenetic diversity, PD) in 2011–2019. There were no significant differences of mean air temperature between C4313 and W4513, or between C4513 and W4693 in 2011–2019, indicating the differences of air temperature were eliminated among elevations. Then we found that the differences of plant α-diversity and ANPP were also eliminated among elevations: (1) there were no significant differences of ANPP, Pielou and MNTD between C4313 and W4513, or between C4513 and W4693 in 2011–2019. (2) There were also no significant differences of mean species richness, Shannon and Simpson between C4513 and W4693 in 2011–2019. (3) There were also no significant differences of ANPP, species richness, Shannon, Simpson, Pielou, PD and MNTD between C4313 and W4513, or C4513 and W4693 in 2019. Therefore, asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production in alpine grasslands, at least in Northern Tibet

Non-growing/growing season non-uniform-warming increases precipitation use efficiency but reduces its temporal stability in an alpine meadow

There are still uncertainties on the impacts of season-non-uniform-warming on plant precipitation use efficiency (PUE) and its temporal stability (PUEstability) in alpine areas. Here, we examined the changes of PUE and PUEstability under two scenes of non-growing/growing season non-uniform-warming (i.e., GLNG: growing-season-warming lower than non-growing-season-warming; GHNG: growing-season-warming higher than non-growing-season-warming) based on a five-year non-uniform-warming of non-growing/growing season experiment. The GLNG treatment increased PUE by 38.70% and reduced PUEstability by 50.47%, but the GHNG treatment did not change PUE and PUEstability. This finding was mainly due to the fact that the GLNG treatment had stronger influences on aboveground biomass (AGB), non-growing-season soil moisture (SMNG), temporal stability of AGB (AGBstability), temporal stability of non-growing-season air temperature (Ta_NG_stability), temporal stability of growing-season vapor pressure deficit (VPDG_stability) and temporal stability of start of growing-season (SGSstability). Therefore, the warming scene with a higher non-growing-season-warming can have greater influences on PUE and PUEstability than the warming scene with a higher growing-season-warming, and there were possibly trade-offs between plant PUE and PUEstability under season-non-uniform-warming scenes in the alpine meadow

Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

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