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Considering the crucial role of soil diazotrophs and root arbuscular mycorrhizal fungi (AMF) in soil nutrient cycling during ecosystem restoration, diazotroph and AMF communities may be determined by slope position. However, the effect of slope position on diazotroph and AMF abundance, diversity, and community composition of karst ecosystems remains unknown. In this study, soil diazotrophs and root AMF characteristics on varying slope positions were assessed in a karst shrub ecosystem. The results displayed that the abundance of soil diazotrophs and root AMF diversity were significantly affected by slope position. Diazotroph abundance accompanied by soil nutrient and plant richness was higher on the lower slopes than the upper slopes, whereas root AMF diversity displayed the opposite trend. The soil diazotroph and root AMF community composition differed among the upper, middle, and lower slopes. The dominant taxa of soil diazotrophs and root AMF at the order level were Rhizobiales and Glomerales, respectively. Moreover, the diazotroph order of Nostocales and the AMF order of Paraglomerales were richer on the upper slopes than on the lower slopes. The slope position directly affected the plant diversity and soil nutrient distribution, indirectly affecting the diazotroph and AMF communities. Increased available nitrogen on the lower slope caused great diazotroph abundance by stimulating plant growth with sufficient carbohydrates. However, low soil nutrients and plant diversity but high plant root biomass induced more root AMF diversity on the upper slope than on the lower slope. Therefore, this study expands the knowledge of soil diazotroph and root AMF ecological functions along different slope positions during vegetation recovery for the successive stages of grass and shrub in the karst region. [ABSTRACT FROM AUTHOR]

Grain for Green project could result in high evapotranspiration (ET), exacerbate water shortage, and intensify conflicts in water demand between ecosystems and humans in many water‐limited areas. However, it is still unclear how vegetation restoration would influence ET in humid karst regions where abundant precipitation but quick leakage. Particularly, it is challenging that existing ET measurement methods are not applicable simultaneously for various ecosystems in such highly heterogeneous and fragmented landscapes. To solve this issue, this study, for the first time, develops a new ET measurement system, by integrating multi‐techniques, to quantify ET for multiple vegetation restoration types and identify their controlling factors in a humid karst region for three consecutive years. The results show that (a) for different succession stages during natural restoration, the ET of secondary forest (680 ± 7 mm yr−1) > shrub (564 ± 16 mm yr−1) > grass (546 ± 12 mm yr−1) > cropland (513 ± 8 mm yr−1) (P < 0.05); (b) ET of all ecosystems are far less than that of precipitation (1,059 ± 63 mm yr−1) but closer to potential ET (849 ± 11 mm yr−1), and soil moisture has been maintained at >0.20 cm3cm−3 in the growing season; (c) solar radiation and vapor pressure deficit are the two predominant factors on the ET (P < 0.01), while there is no significant relationship between ET and soil moisture (P > 0.05). The results suggest that vegetation restoration returned from cropland in humid karst regions increased ET but may not induce water scarcity for vegetation sustainability, at least under normal climatic conditions. Key Points: Vegetation restoration will increase the rate of water consumption, but may not threaten ecosystem's sustainability in humid karst regionEnergy rather than water is the limiting factor for humid karst ecosystem water consumptionA new evapotranspiration measurement system is developed to quantify evapotranspiration for multiple vegetation restoration types [ABSTRACT FROM AUTHOR]

Mapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF‐1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high‐resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency. [ABSTRACT FROM AUTHOR]

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms. The application of deep learning to plant breeding is an active area of research. This work reports a new method, DNNGP, for prediction of quantitative traits from multi-omics data in the context of genomic selection in plants. DNNGP performs as well as or better than several commonly used methods in a broad range of tasks. The insights gained through the development, testing, and application of DNNGP can benefit genomics applications of deep learning in the future. [ABSTRACT FROM AUTHOR]

The power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs) are affected by the presence of Pb-I antisite and I vacancy defects. Here, we propose a strategy for synchronous passivation of defects by Lewis bases D-Tryptophan methyl ester hydrochloride (D-TMe·HCl). The results show that the amino and carboxyl groups in D-TMe·HCl could synchronously passivate the Pb-I antisite and I vacancy defects through ionic and hydrogen bonding. In addition, the inhibition of ion migration of Pb2+ and I− effectively suppresses charge recombination and promotes interfacial carrier extraction. As a result, the PCE of the device with D-TMe·HCl increased to 20.59% with a fill factor of 79.8%. Furthermore, the introduction of D-TMe·HCl promotes the crystallinity of the perovskite and obtains a more dense and uniform film, thus the stability of the device is enhanced. The device with D-TMe·HCl retains more than 94% of its initial PCE after aging in a glovebox for 500 h under nitrogen conditions. This work opens up a new avenue for achieving efficient and stable PSCs by forming high-quality films, synchronously passivating defects, and inhibiting ion migration. [ABSTRACT FROM AUTHOR]

The vegetation is known to be sensitive to both climate change and anthropogenic disturbance. However, the relationship between changes in vegetation and climate is unclear in karst regions. The nonlinear characteristics of vegetation change and its possible relationships with driving factors in the karst region of southwest China are revealed, using methods of Ensemble Empirical Mode Decomposition, Mann-Kendall, and Partial Least Squares Regression. The results show that: (1) vegetation changes demonstrate an increasing trend with an abrupt change in 2002. Multiple time scales of 3, 6, 10, and 25-year are observed in vegetation variations, dominated by long-term trend and the short time scale of 3-year with variance contributions of 58.10% and 28.63%. (2) The relationship of climate indexes with vegetation changes shows r 2 = 0.78 (p < 0.01) based on the reconstruction of characteristic scales, indicating significant great relationship. In space, the area percentage with relationship of climate to vegetation is more than 50%, and the impact is much greater after the abrupt change of vegetation in 2002 (r 2 are 0.24–0.91 and 0.42–0.99, respectively). In addition, the correlation between vegetation change and ecological engineering is 0.15 (p < 0.01). The results indicate that climate change is the main impact factor of vegetation change, ecological engineering has positive influences in improving vegetation condition, and methods of scales decomposition and abrupt detection could reveal some hidden information for better understanding ecosystems in karst regions. [ABSTRACT FROM AUTHOR]

An ex-situ 15 N–18O tracing experiment with soils collected from the valley and slope, respectively, of a subtropical secondary karst forest with three N addition levels, i.e., 0, 50, and 100 kg N ha−1 year−1 for each topographic position to investigate N2O production pathways. Autotrophic nitrification pathways (ammonia oxidation, nitrifier denitrification, and nitrification-coupled denitrification) accounted for > 70% of total N2O production, but denitrification pathways (heterotrophic denitrification and co-denitrification) were the minor source of N2O at both topographic positions. In the valley, chronic N addition stimulated ammonia oxidation-derived N2O, which was paralleled by increased ammonia-oxidizing archaea (AOA) amoA gene transcript abundance, but inhibited nitrifier denitrification- and nitrification-coupled denitrification–derived N2O along with suppressed ammonia-oxidizing bacteria (AOB) amoA gene transcript abundance and stimulated nosZII gene transcript abundance, respectively. On the slope, chronic N addition stimulated ammonia oxidation-derived N2O along with increased AOB amoA gene transcript abundance, and enhanced nitrifier denitrification-derived N2O congruent with increased AOB amoA and decreased nirK gene transcript abundances. In addition, chronic N addition reduced the relative contribution of heterotrophic denitrification to N2O production but had no significant influence on heterotrophic denitrification-derived N2O on the slope. Overall, our results provide a comprehensive view in terms of how topography-driven soil properties regulate N2O production and its pathways in a subtropical forest. [ABSTRACT FROM AUTHOR]

• GNSS on Vancouver Island showed up to 1 cm southeastward postseismic motion. • This far-field postseismic deformation lasted only for 2 – 3 years. • Upper-mantle transient rheology explains the short-lived postseismic deformation. • The lithosphere acted as stress guide against resistance of the asthenosphere. • The observations require the transient rheology to extend to large depths. The Mw 7.8 2012 Haida Gwaii earthquake and the Mw 7.5 2013 Craig earthquake occurred along the Queen Charlotte transpressive margin, two months and 340 km apart. On and near Vancouver Island, hundreds of kilometres southeast of the two events, many continuous Global Navigation Satellite Systems sites underwent no discernable coseismic motion but up to 1 cm southeast postseismic motion (away from epicenters) which lasted for only 2 – 3 years. To resolve the postseismic signal of the two earthquakes, we used a minimum of four years of measurements before the Haida Gwaii earthquake to remove the longer-term motion of these sites that reflect mainly the locking of the Cascadia megathrust. To study the geodynamic process that governed the observed far-field postseismic motion, in particular its short duration, we employ a model of an elastic lithosphere with thickness 20 km and rigidity (μ E) 32 GPa overlying Burgers-viscoelastic mantle layers in a self-gravitating spherical Earth which captures the regionally integrated effect of Earth rheology despite the complex and controversial tectonics of the study area. We find that the short-lived far-field postseismic motion was controlled entirely by the transient rheology. The observations can be explained by a steady-state (Maxwell) viscosity η M of the upper mantle no lower than 1 × 10 19 Pa s with a transient (Kelvin) viscosity η K of about 6 × 10 17 Pa s (Kelvin relaxation time τ K ∼1.2 years) extending to large depths such as > 400 km. The far-field postseismic deformation is the result of the elastic plate serving as a stress guide but resisted by the asthenospheric mantle undergoing transient creep. The lithosphere-asthenosphere coupling is quantified by a "plate Kelvin response time" τ K E = η K / μ E. Rheological parameters can be scaled to other values, but as long as τ K and τ K E stay unchanged, the far-field deformation results remain the same. Our observations presented a unique opportunity to quantify the effect of the transient rheology separately from that of the steady-state rheology. [ABSTRACT FROM AUTHOR]

In this paper, an efficient synthesis of 5‐carbonyl substituted benzo[a]carbazoles via Rh(III)‐catalyzed [5+1] annulation reaction of 2‐aryl‐3‐acyl‐1H‐indoles with α‐diazo carbonyl compounds is presented. The cascade process is accomplished by a C−H bond alkylation, cyclization and aromatization. Notably, this novel method features broad substrate compatibility and good functional group tolerance. In addition, the synthetic applications of this protocol have also been demonstrated by gram‐scale synthesis and derivatization to an antitumor agent. [ABSTRACT FROM AUTHOR]

The hydrological conditions of river-connected lakes are complex primarily owing to their considerable water-level fluctuations (WLFs). Water quality in such lakes varies with hydrodynamic variations; however, their relationship is not clear. To identify the unique relationship between water level and water quality in river-connected lakes, we used the comprehensive pollution index (CPI) and regression analysis to analyze the spatiotemporal variation in water quality in Dongting Lake from 2015 to 2018 and the effects of water level on water quality. Four water quality parameters were selected: total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), and chlorophyll a (Chl-a). The results showed significant spatial variation in the lake water quality, with relatively high concentrations of TN, TP, CODMn, and Chl-a in East Dongting Lake. TN and TP decreased by 12.15% and 37.61%, respectively, from 2015 to 2018, whereas CODMn increased from 1.781 to 2.009 mg/L. Seasonally, TN and TP concentrations were low in the summer and autumn, with high concentrations in the winter and spring. In contrast, CODMn and Chl-a concentrations exhibited opposite trends. The pollution level in Dongting Lake ranged between slightly and moderately polluted, with a CPI ranging from 0.76 to 1.32 across all sampling sites during 2015–2018. The water level in Dongting Lake initially increased and, then, decreased in a year, with marked WLFs owing to seasonal shifts in precipitation and human activities. The water level had significant negative relationships with TN and TP concentrations and a significant positive relationship with CODMn concentration (p < 0.05). Based on the results, strict control of excessive external nutrient loading should be actively implemented in Dongting Lake, in addition to hydrological regulation for effective lake water quality management. [ABSTRACT FROM AUTHOR]