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

Acceleration without Disruption: DFT Software as a Service

Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy. It provides an accessible and scalable solution for the increasing demands of DFT calculations in scientific communities. The implementation details, examples, and benchmark results illustrate how Accelerated DFT can significantly expedite scientific discovery across various domains

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.</jats:p

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.</jats:p

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

DataSheet_1_Non-growing/growing season non-uniform-warming increases precipitation use efficiency but reduces its temporal stability in an alpine meadow.docx

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.</p

Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

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 (AGBd). This study aims to predict grassland AGB in Xizang at both the plant taxonomic group (sedge, graminoid, forb) and community levels, from both an AGBd and a fresh weight (AGBf) perspective. Three to four independent variables (growing mean temperature, total precipitation, total radiation and NDVImax, 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 NDVImax. 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