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Identifying the impact path of climate and soil factors on soil δ15N is very crucial for better understanding the N turnover in soils and the integrated information about ecosystem N cycling. Many studies have showed that climate and soil variables influence the change of soil δ15N. However, most of the existing studies focused on the overall impact of factor on soil δ15N, without distinguishing between the direct and indirect effect. Although scholars have studied the relationships among temperature, precipitation, soil N, soil pH, and soil δ15N rather than estimating all the causal relationships simultaneously. To answer the above-mentioned questions, a regional-scale soil collection was conducted across a temperate grassland in northern China. Meanwhile, a PLS-PATH analysis was utilized to evaluate the direct and indirect effects of various factors on soil δ15N and to explore the causal relationships among variables. The results showed that along the transect, mean annual precipitation (MAP) and mean annual temperature (MAT) directly and significantly reduced soil δ15N, and indirectly affected soil δ15N through their effects on soil pH, soil clay, soil N and soil C/N. Soil C/N ratio has a significant direct impact on soil δ15N with a negative correlation. Soil clay, soil N content, and soil pH have a total positive effect on soil δ15N, but the total positive impact of soil pH is very weak because it has a negative indirect impact on soil δ15N by affecting soil clay, soil N and soil C/N ratio. The total influence is, in order, MAP > MAT > soil C/N > soil clay > soil N > soil pH (in absolute value). The above results will provide valuable information about ecosystem N cycle in temperate grassland of northern China. [ABSTRACT FROM AUTHOR]

Screening of available local halophytes for salinity tolerance is of considerable economic value for the utilization of heavy saltaffected lands in coastal tidal-flat areas and other saline areas. In this study, the germination and seedling pot experiments on salt tolerance of eight halophytic species from Yellow River Delta, China, at seven NaCl concentrations (0, 50, 100, 150, 200, 250, and 300 mM), were conducted at both growth stages. Results showed that germination rate and germination index decreased with an increase in NaCl concentration. The higher germination rates were obtained from Tamarix chinensis and Suaeda salsa seeds exposed to 0∼200mM NaCl. At the seedling stage, the salt tolerances of eight halophytes were also different from each other. Tamarix chinensis had significantly greater fresh biomass and plant height in relative terms than the others in all salt treatments. The order of the relative growth yield in seedling was Tamarix chinensis > Suaeda salsa > Salicornia europaea > Limonium bicolor > Atriplex isatidea > Apocynum venetum > Phragmites australis > Sesbania cannabina. The comprehensive analysis showed that Tamarix chinensis had the highest tolerance to salt, followed by Suaeda salsa, and the salt tolerance of Sesbania cannabina was the lowest. [ABSTRACT FROM AUTHOR]

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem. [ABSTRACT FROM AUTHOR]

Abstract: Carbon isotope values (δ13C) of plants reflect the balance between photosynthesis and stomatal conductance and vary with environmental conditions. Temperature is an important influential factor for plant δ13C; however, variations of plant δ13C across temperature gradients have yet to be quantified. This impedes interpretation of variability of δ13C in ancient terrestrial organic matter and reconstructions of paleoclimate and paleoecology. In this study, the effect of temperature on δ13C of C3 and C4 plants was assessed by comprehensive investigation of a large number of species (118 C3 plants and 17 C4 plants) from 29 sites across a temperature gradient along the 400 mm mean annual precipitation isoline in north China. After deducting the effects of precipitation on plant δ13C, a strong positive relationship is found between δ13C values of C3 plants averaged for each site and mean annual temperature, with a coefficient of 0.104‰/°C; however, no significant correlation is found for C4 plants. Based on our results, we conduct a sample reconstruction of paleovegetation of the central Chinese Loess Plateau and demonstrate that, to a certain extent, our paleovegetation reconstruction is affected by temperature corrections. [Copyright &y& Elsevier]

Tree mortality in response to climate change induced drought has emerged as a global concern. Small changes of tree mortality rates can profoundly affect forest structure, composition, dynamics and ecosystem services such as carbon sequestration. Our analyses of longitudinal data from natural stands (82 plots) in Beijing showed that tree mortality rates have increased significantly over the two decades from 1986 to 2006. In contrast, recruitment rates decreased significantly over this period. The increase in overall mortality rates resulted from an increase in tree deaths dominantly attributed to changes in temperature and precipitation resulting in drier conditions across latitudes, elevations, tree species, and tree sizes. In addition, the results showed that mortality rates of Chinese pine ( Pinus tabuliformis) ( β = 0.0874) as a result of climate change induce drought were much smaller than oak ( Quercus) ( β = 0.1583). [ABSTRACT FROM AUTHOR]

The tree mortality model plays an important role in simulating stand dynamic processes. Past work has shown that the disaggregation method was successful in improving tree survival prediction. This method was used in this study to forecast tree survival probability of Chinese pine ( Pinus tabulaeformis Carrière) in Beijing. Outputs from the tree survival model were adjusted from either the stand-level model prediction or the combined estimator from the forecast combination method. Our results show that the disaggregation approach improved the performance of tree survival models. We also showed that stand-level prediction played a crucial role in refining outputs from a tree survival model, especially when it is a very simple model. Because the forecast combination method produced better stand-level prediction, we prefer the use of this method in conjunction with the disaggregation approach, even though the performance gain in using the forecast combination method shown for this data set was modest. [ABSTRACT FROM AUTHOR]