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Summary: Phosphorus (P) fertilization can alleviate a soil P deficiency in grassland ecosystems. Understanding plant functional traits that enhance P uptake can improve grassland management.We measured impacts of P addition on soil chemical and microbial properties, net photosynthetic rate (Pn) and nonstructural carbohydrate concentrations ([NSC]), and root P‐uptake rate (PUR), morphology, anatomy, and exudation of two dominant grass species: Leymus chinensis (C3) and Cleistogenes squarrosa (C4).For L. chinensis, PUR and Pn showed a nonlinear correlation. Growing more adventitious roots compensated for the decrease in P transport per unit root length, so that it maintained a high PUR. For C. squarrosa, PUR and Pn presented a linear correlation. Increased Pn was associated with modifications in root morphology, which further enhanced its PUR and a greater surplus of photosynthate and significantly stimulated root exudation (proxied by leaf [Mn]), which had a greater impact on rhizosheath micro‐environment and microbial PLFAs.Our results present correlations between the PUR and the Pn of L. chinensis and C. squarrosa and reveal that NSC appeared to drive the modifications of root morphology and exudation; they provide more objective basis for more efficient P‐input in grasslands to address the urgent problem of P deficiency. [ABSTRACT FROM AUTHOR]

Background: Grazing is an important land use in northern China. In general, different grazing intensities had a different impact on the morphological and physiological traits of plants, and especially their photosynthetic capacity. We investigated the responses of Leymus chinensis to light, medium, and heavy grazing intensities in comparison with a grazing exclusion control. Results: With light grazing, L. chinensis showed decreased photosynthetic capacity. The low chlorophyll and carotenoid contents constrained light energy transformation and dissipation, and Rubisco activity was also low, restricting the carboxylation efficiency. In addition, the damaged photosynthetic apparatus accumulated reactive oxygen species (ROS). With medium grazing, more energy was used for thermal dissipation, with high carotene content and high non-photochemical quenching, whereas photosynthetic electron transport was lowest. Significantly decreased photosynthesis decreased leaf C contents. Plants decreased the risk caused by ROS through increased energy dissipation. With high grazing intensity, plants changed their strategy to improve survival through photosynthetic compensation. More energy was allocated to photosynthetic electron transport. Though heavy grazing damaged the chloroplast ultrastructure, adjustment of internal mechanisms increased compensatory photosynthesis, and an increased tiller number facilitated regrowth after grazing. Conclusions: Overall, the plants adopted different strategies by adjusting their metabolism and growth in response to their changing environment. [ABSTRACT FROM AUTHOR]

Seasonal non-structural carbohydrate (NSC) dynamics in different organs can indicate the strategies trees use to cope with water stress; however, these dynamics remain poorly understood along a large precipitation gradient. In this study, we hypothesized that the correlation between water availability and NSC concentrations in different organs might be strengthened by decreasing precipitation in Pinus tabulaeformis Carr. forests in temperate China. Our results show that the concentrations of soluble sugars were lower in stems and coarse roots, and starch was higher in branches in the early growing season at drier sites. Throughout the growing season, the concentrations of soluble sugars increased in drier sites, especially for leaves, and remained stable in wetter sites, while starch concentrations were relatively stable in branches and stems at all sites. The NSC concentrations, mainly starch, decreased in coarse roots along the growing season at drier sites. Trees have a faster growth rate with an earlier cessation in active stem growth at drier sites. Interestingly, we also found a divergent relationship between NSCs in different organs and mean growing season water availability, and a stronger correlation was observed in drier sites. These results show that pine forests in arid and semi-arid regions of northern China exhibit different physiological responses to water availability, improving our understanding of the adaptive mechanisms of trees to water limitations in a warmer and drier climate. [ABSTRACT FROM AUTHOR]

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs. [ABSTRACT FROM AUTHOR]