Hydrogen and oxygen isotope composition and water quality evaluation for different water bodies in the Ebinur Lake Watershed, Northwestern China

Wetlands are sensitive indicators of climate change and have a profound impact on the supply of water resources in surrounding areas. In this study, the hydrochemical, isotopic characteristics (δ18O and δ2H) of groundwater and surface water (lake, reservoir, and river) in the Ebinur Lake Watershed, northwestern China, were investigated to reveal the relationships between various water bodies. The results suggest that the groundwater is alkaline and has pH and total dissolved solids (TDS) values less than those of surface water. Ca2+ and SO42- are the major ions in the groundwater and river water, whereas lake water and reservoir water are enriched in Na+ and SO42-. With the decrease in elevation, both groundwater and river water are affected by carbonate dissolution at high elevation and by evaporitic rock dissolution at low elevation; thus, the water surrounding Ebinur Lake is subjected to runoff affected by intense evaporation-dissolution of evaporitic rocks. The stable isotope compositions suggested that the upstream part of the river is recharged by glacial meltwater from high mountains, whereas the middle-downstream parts of the river are recharged by low-elevation precipitation. Shallow groundwater and reservoir water are mainly recharged by river water and are more enriched in the downstream part of river. Water samples were also classified according to different indices, such as chemical oxygen demand (COD), NH3-N, volatile phenol, sulfate, Zn, Co, Cu, total hardness, and Cr6+, and results showed that most groundwater is suitable for drinking and irrigation purposes. Except for Cr6+, the metal concentrations are within permissible limits. However, both groundwater and reservoir water are affected to some extent by nearby rivers from anthropogenic activity

Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R2) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water

Digital Mapping of Root-Zone Soil Moisture Using UAV-Based Multispectral Data in a Kiwifruit Orchard of Northwest China

Accurate estimation of root-zone soil moisture (SM) is of great significance for accurate irrigation management. This study was purposed to identify planted-by-planted mapping of root-zone SM on three critical fruit growth periods based on UAV multispectral images using three machine learning (ML) algorithms in a kiwifruit orchard in Shaanxi, China. Several spectral variables were selected based on variable importance (VIP) rankings, including reflectance Ri at wavelengths 560, 668, 740, and 842 nm. Results indicated that the VIP method effectively reduced 42 vegetation indexes (VIs) to less than 7 with an evaluation accuracy of root-zone SM models. Compared with deep root-zone SM models (SM40 and SM60), shallow root-zone SM models (SM10, SM20, and SM30) have better performance (R2 from 0.65 to 0.82, RRMSE from 0.02 to 0.03, MAE from 0.20 to 0.54) in the three fruit growth stages. Among three ML algorithms, random forest models were recommended for simulating kiwi root-zone SM during the critical fruit growth period. Overall, the proposed planted-by-planted root-zone SM estimation approach can be considered a great tool to upgrade the toolbox of the growers in site-specific field management for the high spatiotemporal resolution of SM maps

Leaf mechanical strength and photosynthetic capacity vary independently across 57 subtropical forest species with contrasting light requirements

Leaf mechanical strength and photosynthetic capacity are critical plant life-history traits associated with tolerance and growth under various biotic and abiotic stresses. In principle, higher mechanical resistance achieved via higher relative allocation to cell walls should slow photosynthetic rates. However, interspecific relationships among these two leaf functions have not been reported. We measured leaf traits of 57 dominant woody species in a subtropical evergreen forest in China, focusing especially on photosynthetic rates, mechanical properties, and leaf lifespan (LLS). These species were assigned to two ecological strategy groups: shade-tolerant species and light-demanding species. On average, shade-tolerant species had longer LLS, higher leaf mechanical strength but lower photosynthetic rates, and exhibited longer LLS for a given leaf mass per area (LMA) or mechanical strength than light-demanding species. Depending on the traits and the basis of expression (per area or per mass), leaf mechanical resistance and photosynthetic capacity were either deemed unrelated, or only weakly negatively correlated. We found only weak support for the proposed trade-off between leaf biomechanics and photosynthesis among co-occurring woody species. This suggests there is considerable flexibility in these properties, and the observed relationships may result more so from trait coordination than any physically or physiologically enforced trade-off

How do functional traits influence tree demographic properties in a subtropical monsoon forest?

Functional traits are good predictors of plant responses and adaptations to ever-changing environments. However, forecasting forest community dynamics is challenging because the relationships among different tree demographic properties (growth, mortality and recruitment) and how functional traits are associated with tree demography remain largely unknown. Here, in a 20-ha subtropical forest permanent plot, we quantified the rates of tree growth, mortality and recruitment across 53 dominant tree species (diameter at breast height; DBH ≥ 1 cm) from 2005 to 2020. Functional traits that are closely related to plant photosynthesis, nutrients, hydraulics and drought tolerance were measured. We found that tree growth rate (GR) varied independently from rates of tree mortality and recruitment. Hydraulic conductivity was positively correlated with GR (explaining 27% variation—the strongest relationship observed) whereas wood density was negatively correlated with GR. Leaf life span was negatively related to tree mortality. Species with high carbon assimilation rate, nutrient concentration and hydraulic conductivity had high recruitment rates. Leaf turgor loss point was unrelated to plant demography. Principal component analysis revealed that species with quick resource acquisition rates had high rates of growth and recruitment. Our results illustrate that the correlations among tree demographic properties were weak in this subtropical forest with monsoonal climate. Most notably, against expectations, there was no observed trade-off between growth and mortality. Individual functional traits explained up to 27% of each demographic rate. Variation in recruitment rate was aligned with traits indexing the leaf economic spectrum and also plant hydraulic variation. A better understanding of the role of disturbances on trait–demography relationships would help build a deeper and more nuanced understanding of the ecology of subtropical monsoon forests. Read the free Plain Language Summary for this article on the Journal blog

Growing-season temperature and precipitation are independent drivers of global variation in xylem hydraulic conductivity

Stem xylem-specific hydraulic conductivity (KS) represents the potential for plant water transport normalized by xylem cross-section, length, and driving force. Variation in KS has implications for plant transpiration and photosynthesis, growth and survival, and also the geographic distribution of species. Clarifying the global-scale patterns of KS and its major drivers are needed to achieve a better understanding of how plants adapt to different environmental conditions, particularly under climate change scenarios. Here, we compiled a xylem hydraulics dataset with 1186 species-at-site combinations (975 woody species representing 146 families, from 199 sites worldwide), and investigated how KS varied with climatic variables, plant functional types, and biomes. Growing-season temperature and growing-season precipitation drove global variation in KS independently. Both the mean and the variation in KS were highest in the warm and wet tropical regions, and lower in cold and dry regions, such as tundra and desert biomes. Our results suggest that future warming and redistribution of seasonal precipitation may have a significant impact on species functional diversity, and is likely to be particularly important in regions becoming warmer or drier, such as high latitudes. This highlights an important role for KS in predicting shifts in community composition in the face of climate change