The Effects of Plant and Soil Characteristics on Partitioning Different Rainfalls to Soil in a Subtropical Chinese Fir Forest Ecosystem

Mineral Nutrient Uptake, Accumulation, and Distribution in Cunninghamia lanceolata in Response to Drought Stress

Mineral accumulation in plants under drought stress is essential for drought tolerance. The distribution, survival, and growth of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), an evergreen conifer, can be affected by climate change, particularly seasonal precipitation and drought. Hence, we designed a drought pot experiment, using 1-year-old Chinese fir plantlets, to evaluate drought effects under simulated mild drought, moderate drought, and severe drought, which corresponds to 60%, 50%, and 40% of soil field maximum moisture capacity, respectively. A treatment of 80% of soil field maximum moisture capacity was used as control. Effects of drought stress on mineral uptake, accumulation, and distribution in Chinese fir organs were determined under different drought stress regimes for 0-45 days. Severe drought stress significantly increased phosphorous (P) and potassium (K) uptake at 15, 30 and 45 days, respectively, within fine (diameter < 2 mm), moderate (diameter 2-5 mm), and large (diameter 5-10 mm) roots. Drought stress decreased magnesium (Mg) and manganese (Mn) uptake by fine roots and increased iron (Fe) uptake in fine and moderate roots but decreased Fe uptake in large roots. Severe drought stress increased P, K, calcium (Ca), Fe, sodium (Na), and aluminum (Al) accumulation in leaves after 45 days and increased Mg and Mn accumulation after 15 days. In stems, severe drought stress increased P, K, Ca, Fe, and Al in the phloem, and P, K, Mg, Na, and Al in the xylem. In branches, P, K, Ca, Fe, and Al concentrations increased in the phloem, and P, Mg, and Mn concentrations increased in the xylem under severe drought stress. Taken together, plants develop strategies to alleviate the adverse effects of drought stress, such as promoting the accumulation of P and K in most organs, regulating minerals concentration in the phloem and xylem, to prevent the occurrence of xylem embolism. The important roles of minerals in response to drought stress should be further evaluated.

Contrasting water-use patterns of Chinese fir among different plantation types in a subtropical region of China

Plantation cultivation plays an important role in improving terrestrial ecosystem functions and services. Understanding the water-use patterns of major afforestation species is vital for formulating ecological restoration strategies and predicting the response of plantation to climate change. However, the impacts and drivers of forest types on water-use patterns of key tree species are poorly understood. Here, the combined methods of dual stable isotope of δD and δ ¹⁸O and Bayesian mixed framework (MixSIAR) were employed to investigate the water-use patterns of Cunninghamia lanceolata (Chinese fir) in a monoculture, mixed forest with Cinnamomum camphora, and mixed forest with Alnus cremastogyne under different rainfall events in subtropical China. Furthermore, the relative contribution of different soil and plant factors to the water-use patterns of Chinese fir was quantified using a random forest model. Our results showed that Chinese fir in the mixed forests (with C. camphora or with A. cremastogyne) utilized less water from shallow soil compared to that in a monoculture but significantly improved the proportion of water absorbed from deep soil with the increase of 55.57%-64.90% and 68.99%-108.83% following moderate and heavy rainfall events, respectively. The most important factors contributing to the differences in water-use patterns of Chinese fir among monoculture and mixed forests were tree attributes (i.e., leaf biomass, eco-physiological regulation, and fine root biomass). These findings reveal that Chinese fir in mixed forests could optimize water-use patterns by adjusting plant properties for interspecific niche complementarity, improving the utilization of deep soil water. Overall, this study suggests that mixed-species plantations could improve water-use efficiency and reduce the sensitivity of tree species to precipitation change, indicating they are better able to cope with expected climate variability.

Landslide Susceptibility Research Combining Qualitative Analysis and Quantitative Evaluation: A Case Study of Yunyang County in Chongqing, China

Machine learning-based methods are commonly used for landslide susceptibility mapping. Most of the recent publications focused on quantitative analysis, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into consideration and the further analysis of the key features was always lacking. This study aims to combine qualitative and quantitative analysis and examine its effect on mapping accuracy; based on the feature importance ranks and the related literature, the key features for identifying landslide/non-landslide points of different sub-zones were further analyzed. Before modeling, the study area Yunyang County, Chongqing City, China, was manually divided into four sub-zones based on the information from geological hazards exploration in Chongqing, including the mechanism of landslide formation and sliding failure and geomorphic unit characteristics. Upon the qualitative analysis basis, five grid searches tuned random forest models (one for the whole region and four for the sub-zones independently) were established by 1654 data points and 20 conditioning features. Compared with the conventional data-driven method, the integrated quantitative evaluation based on the qualitative analysis results showed higher reliability, which not only improved the mapping accuracy but also increased the AUC values of all four sub-models, which were 8.8%, 2.3%, 1.9% and 9.1% higher than that of the parent model. Moreover, the quantitative evaluation based on the qualitative analysis revealed the key factors affecting local landslide formation. Therefore, qualitative analysis is recommended in future landslide susceptibility modeling with the additional combination of data-driven methods.