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Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha−1) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha−1), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha−1) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha−1) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha−1), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha−1). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha−1) and 75.3% (7.0 ± 0.38 Mg ha−1) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Human mutations in the gene encoding the solute carrier (SLC) 6A17 caused intellectual disability (ID). The physiological role of SLC6A17 and pathogenesis of SLC6A17-based-ID were both unclear. Here, we report learning deficits in Slc6a17 knockout and point mutant mice. Biochemistry, proteomic, and electron microscopy (EM) support SLC6A17 protein localization in synaptic vesicles (SVs). Chemical analysis of SVs by liquid chromatography coupled to mass spectrometry (LC-MS) revealed glutamine (Gln) in SVs containing SLC6A17. Virally mediated overexpression of SLC6A17 increased Gln in SVs. Either genetic or virally mediated targeting of Slc6a17 reduced Gln in SVs. One ID mutation caused SLC6A17 mislocalization while the other caused defective Gln transport. Multidisciplinary approaches with seven types of genetically modified mice have shown Gln as an endogenous substrate of SLC6A17, uncovered Gln as a new molecule in SVs, established the necessary and sufficient roles of SLC6A17 in Gln transport into SVs, and suggested SV Gln decrease as the key pathogenetic mechanism in human ID.

Abstract Background Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink and its involving processes by natural ecosystems remain difficult to quantify. Methods Using a transient traceability framework, we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under RCP8.5. Results The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total, among which, Russia, Canada, USA, China, and Brazil sequester the most. This C sink consists of four components: production-driven change, turnover-driven change, change in instantaneous C storage potential, and interaction between production-driven change and turnover-driven change. The four components account for 49.5%, 28.1%, 14.5%, and 7.9% of the land C sink, respectively. Conclusion The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions, especially for countries where net primary production (NPP) likely increases substantially and inherent residence time elongates.

Abstract Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

Mechanisms and consequences of the acclimation of soil respiration to warming are unclear. Here, the authors combine soil respiration, metagenomics, and functional gene results from a 7-year grassland warming experiment to a microbial-enzyme decomposition model, showing functional gene information to lower uncertainty and improve fit.

Abstract Shifts in dominance and species reordering can occur in response to global change. However, it is not clear how altered precipitation and disturbance regimes interact to affect species composition and dominance. We explored community‐level diversity and compositional similarity responses, both across and within years, to a manipulated precipitation gradient and annual clipping in a mixed‐grass prairie in Oklahoma, USA. We imposed seven precipitation treatments (five water exclusion levels [−20%, −40%, −60%, −80%, and −100%], water addition [+50%], and control [0% change in precipitation]) year‐round from 2016 to 2018 using fixed interception shelters. These treatments were crossed with annual clipping to mimic hay harvest. We found that community‐level responses were influenced by precipitation across time. For instance, plant evenness was enhanced by extreme drought treatments, while plant richness was marginally promoted under increased precipitation. Clipping promoted species gain resulting in greater richness within each experimental year. Across years, clipping effects further reduced the precipitation effects on community‐level responses (richness and evenness) at both extreme drought and added precipitation treatments. Synthesis: Our results highlight the importance of studying interactive drivers of change both within versus across time. For instance, clipping attenuated community‐level responses to a gradient in precipitation, suggesting that management could buffer community‐level responses to drought. However, precipitation effects were mild and likely to accentuate over time to produce further community change.

Abstract Grassland ecosystems provide essential services to society. To maintain ecosystem functions and services of grasslands under changing environments, it is critical to understand how grasslands respond and feedback to climate change. Here, we present results from a long‐term (16 years) warming and clipping (to mimic hay harvesting or grazing) experiment conducted in a grassland ecosystem dominated by C4 grasses in the Great Plains, USA. We analyzed responses of net primary production (NPP), aboveground NPP (ANPP), and belowground NPP (BNPP) to the expanded ranges of climate conditions observed in the experiment. NPP, ANPP, and BNPP all responded to the climatic variables nonlinearly, with higher degrees of nonlinearity for BNPP than that for ANPP. BNPP peaked at the optimum air temperature of 17.5–18.5°C while ANPP peaked at the optimum air temperature of 18.5–20.0°C. The optimum air temperatures for both ANPP and BNPP were higher than mean annual temperature (16.3°C) at the site. The optimum precipitation for BNPP was intermediate levels (775–1250 mm) while ANPP was maximized in wetter conditions (1250–1605 mm) compared with BNPP. Clipping significantly reduced ANPP and shifted the optimum conditions for BNPP to warmer and wetter conditions. In summary, C4‐grass dominant ecosystems have the potential to increase NPP in future warmer and wetter conditions, and clipping may amplify this positive effect in this grassland ecosystem.

Peatlands contain one-third of global soil carbon (C), but the responses of peatland ecosystems to long-term warming are not well understood. Here, we pursue an emergent understanding of warming effects on ecosystem C fluxes at peatlands by constraining a process-oriented model, the terrestrial ECOsystem model, with observational data from a long-term warming experiment at the Spruce and Peatland Responses Under Changing Environments site. Model-based assessments show that ecosystem-level photosynthesis and autotrophic respiration exhibited significant thermal acclimation, with temperature sensitivities being linearly decreased with warming. Using the thermal-acclimated parameter values, simulated gross primary production, net primary production, and plant autotrophic respiration ( R _a ), were all lower than those simulated with non-thermal acclimated parameter values. In contrast, ecosystem respiration simulated with thermal acclimated parameter values was higher than that simulated with non-thermal acclimated parameter values. Net ecosystem CO _2 exchange was much higher after constraining model parameters with observational data from the warming treatments, releasing C at a rate of 28.3 g C m ^−2 yr ^−1 °C ^−1 . Our data-model integration study suggests that peatlands are likely to release more C than previously estimated. Earth system models may overestimate C uptake by peatlands under warming if physiological thermal acclimation of plants is not incorporated. Thus, it is critical to consider the long-term physiological thermal acclimation of plants in the models to better predict global C dynamics under future climate and their feedback to climate change.

Plants are thought to be limited by phosphorus (P) especially in tropical regions. Here, Hou et al. report a meta-analysis of P fertilization experiments to show widespread P limitation on plant growth across terrestrial ecosystems modulated by climate, ecosystem properties, and fertilization regimes

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R2 = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.

Many animals exhibit an astonishing ability to form groups of large numbers of individuals. The dynamic properties of such groups have been the subject of intensive investigation. The actual grouping processes and underlying neural mechanisms, however, remain elusive. Here, we established a social clustering paradigm in Drosophila to investigate the principles governing social group formation. Fruit flies spontaneously assembled into a stable cluster mimicking a distributed network. Social clustering was exhibited as a highly dynamic process including all individuals, which participated in stochastic pair-wise encounters mediated by appendage touches. Depriving sensory inputs resulted in abnormal encounter responses and a high failure rate of cluster formation. Furthermore, the social distance of the emergent network was regulated by ppk-specific neurons, which were activated by contact-dependent social grouping. Taken together, these findings revealed the development of an orderly social structure from initially unorganised individuals via collective actions.

Species diversity is thought to play an important role in maintaining production stability. Shi et al.demonstrate that the dominant C4 plant also makes a substantial contribution to temporal stability in a grassland ecosystem subject to 15 years of experimental warming and hay harvest.

Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and achieved positive practical results, which provides us with a new opportunity to update the APED algorithm. We first constructed an end-to-end ASR system based on the hybrid connectionist temporal classification and attention (CTC/attention) architecture. An adaptive parameter was used to enhance the complementarity of the connectionist temporal classification (CTC) model and the attention-based seq2seq model, further improving the performance of the ASR system. After this, the improved ASR system was used in the APED task of Mandarin, and good results were obtained. This new APED method makes force alignment and segmentation unnecessary, and it does not require multiple complex models, such as an acoustic model or a language model. It is convenient and straightforward, and will be a suitable general solution for L1-independent computer-assisted pronunciation training (CAPT). Furthermore, we find that find that in regards to accuracy metrics, our proposed system based on the improved hybrid CTC/attention architecture is close to the state-of-the-art ASR system based on the deep neural network−deep neural network (DNN−DNN) architecture, and has a stronger effect on the F-measure metrics, which are especially suitable for the requirements of the APED task.

Tundra is one of the most sensitive biomes to climate warming. Understanding plant eco-physiological responses to warming is critical because these traits can give feedback on the effects of climate-warming on tundra ecosystem. We used open-top chambers following the criteria of the International Tundra Experiment to passively warm air and soil temperatures year round in alpine tundra. Leaf size, photosynthesis and anatomy of three dominant species were investigated during the growing seasons after 7 years of continuous warming. Warming increased the maximal light-saturated photosynthetic rate (Pmax) by 43.6% for Dryas. octopetala var. asiatica and by 26.7% for Rhododendron confertissimum across the whole growing season, while warming did not significantly affect the Pmax of V. uliginosum. The leaf size of Dr. octopetala var. asiatica and Rh. confertissimum was increased by warming. No marked effects of warming on anatomical traits of Dr. octopetala var. asiatica were observed. Warming decreased the leaf thickness of Rh. confertissimum and Vaccinium uliginosum. This study highlights the species-specific responses to climate warming. Our results imply that Dr. octopetala var. asiatica could be more dominant because it, mainly in terms of leaf photosynthetic capacity and size, seems to have advantages over the other two species in a warming world.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO _2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO _2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO _2 uptake since late 1990s. Here, based on in situ atmospheric CO _2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO _2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO _2 concentration ([CO _2 ] _min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO _2 concentration.

Plantago virginica L. has invaded many lawn ecosystems in the Eastern part of China. The invasion has incurred an economic cost to remove them. In order to prevent the invasion, it is critical to understand the invasive mechanisms of this species. However, few studies have been conducted on the allelopathic mechanisms of its invasion. In this study, we examined allelopathic effects of P. virginica on germination of seeds and growth of seedlings of four widely used lawn species. We found extensive allelopathic potential of P. virginica on other lawn species, which varied with species and developmental stage. While most effects of the extracts of P. virginica were inhibitory, some variables in some species were promoted by the addition of the extracts. The extracts of P. virginica significantly inhibited seed germination of Agrostis matsumurae. While the overall differences in seed germination rate of Poa annua were significant among treatments, difference between control and any of the treatments was not significant. The height of seedlings of A. matsumurae and Cynodon dactylon was significantly lower under the treatments of adding extracts of P. virginica. In contrast, growth of seedlings of Festuca elata and P. annua did not show significant differences among treatments. The root length of A. matsumurae, C. dactylon and P. annua was suppressed by the extracts of P. virginica whereas root length of F. elata was not affected. Aboveground biomass of A. matsumurae and F. elata was significantly higher than control, except for F. elata at the concentration of 50mg/mL, whereas aboveground biomass of C. dactylon and P. annua was reduced at higher concentrations of the extracts. Except for A. matsumurae, root biomass of the other three lawn species declined under the treatments with the extracts of P. virginica. Our results revealed that P. virginica had allelopathic potential on four lawn species and supported the theory of "novel weapons hypothesis". Invasion by P. virginica in lawn can be moderated by selecting those species that are not affected or promotionally affected by it.

Plant-microbe interactions are considered to be important processes determining the efficiency of phytoremediation of petroleum pollution, however relatively little is known about how these interactions are influenced by petroleum pollution. In this experimental study using a microcosm approach, we examined how plant ecophysiological traits, soil nutrients and microbial activities were influenced by petroleum pollution in Phragmites australis, a phytoremediating species. Generally, petroleum pollution reduced plant performance, especially at early stages of plant growth. Petroleum had negative effects on the net accumulation of inorganic nitrogen from its organic forms (net nitrogen mineralization (NNM)) most likely by decreasing the inorganic nitrogen available to the plants in petroleum-polluted soils. However, abundant dissolved organic nitrogen (DON) was found in petroleum-polluted soil. In order to overcome initial deficiency of inorganic nitrogen, plants by dint of high colonization of arbuscular mycorrhizal fungi might absorb some DON for their growth in petroleum-polluted soils. In addition, through using a real-time polymerase chain reaction method, we quantified hydrocarbon-degrading bacterial traits based on their catabolic genes (i.e. alkB (alkane monooxygenase), nah (naphthalene dioxygenase) and tol (xylene monooxygenase) genes). This enumeration of target genes suggests that different hydrocarbon-degrading bacteria experienced different dynamic changes during phytoremediation and a greater abundance of alkB was detected during vegetative growth stages. Because phytoremediation of different components of petroleum is performed by different hydrocarbon-degrading bacteria, plants' ability of phytoremediating different components might therefore vary during the plant life cycle. Phytoremediation might be most effective during the vegetative growth stages as greater abundances of hydrocarbon-degrading bacteria containing alkB and tol genes were observed at these stages. The information provided by this study enhances our understanding of the effects of petroleum pollution on plant-microbe interactions and the roles of these interactions in the phytoremediation of petroleum-polluted soil.