Decoupling the influence of vegetation and climate on intra-annual variability in runoff in karst watersheds

Disentangling influences of driving forces on intra-annual variability in sediment discharge in karst watersheds

The intra-annual variability in sediment discharge was considerably influenced by the climate variability and vegetation dynamics. Because of the coupled or relationships between climatic and vegetation variables, it is still challenging to decouple the direct and indirect effects of climate variability and vegetation dynamics on hydrological and sediment transport processes. The purpose of this study is to decouple influences of individual driving force on intra-annual distribution of sediment discharge during 2003-2017 using the partial least squares structural equation model (PLS-SEM) method in four typical karst watersheds of Southwest China. The coefficient of variation (Cv), Completely regulation coefficient (Cr), Lorenz asymmetry coefficient and Gini coefficient were used to represent the intra-annual sediment discharge variability. Results showed that the monthly sediment discharge (190 % < Cv < 353 %) exhibited greater variability than its potential affecting factors (18 % < Cv < 101 %). From the PLS-SEM analysis, the water discharge, climate, and vegetation together explain 57 %-75 %, 64 %-79 %, and 53 %-80 % of the total variance in Cv, Cr, and Gini coefficient, respectively. Specifically, water discharge exerts the largest influence on sediment discharge variability (0.65 ≤ direct effect ≤0.97, P < 0.05), while vegetation dynamic mainly indirectly affects sediment discharge variability (-0.88 ≤ indirect effect ≤ -0.01) through influencing water discharge. The climate factors also principally indirectly affect the sediment discharge variability (-0.47 ≤ indirect effect ≤0.19) by affecting water discharge and vegetation. The PLS-SEM can effectively reveal the driving force and influencing mechanism of intra-annual sediment discharge changes, and provide an important reference for regional soil and water resources management in karst watersheds. Future studies can decouple the influences of the extreme climate, unique lithology, discontinuous soil, heterogeneous landscape, and special geomorphology on spatial variability in sediment discharge across different karst watersheds.

Analysis of the fatigue status of medical security personnel during the closed-loop period using multiple machine learning methods: a case study of the Beijing 2022 Olympic Winter Games

Using machine learning methods to analyze the fatigue status of medical security personnel and the factors influencing fatigue (such as BMI, gender, and wearing protective clothing working hours), with the goal of identifying the key factors contributing to fatigue. By validating the predicted outcomes, actionable and practical recommendations can be offered to enhance fatigue status, such as reducing wearing protective clothing working hours. A questionnaire was designed to assess the fatigue status of medical security personnel during the closed-loop period, aiming to capture information on fatigue experienced during work and disease recovery. The collected data was then preprocessed and used to determine the structural parameters for each machine learning algorithm. To evaluate the prediction performance of different models, the mean relative error (MRE) and goodness of fit (R2) between the true and predicted values were calculated. Furthermore, the importance rankings of various parameters in relation to fatigue status were determined using the RF feature importance analysis method. The fatigue status of medical security personnel during the closed-loop period was analyzed using multiple machine learning methods. The prediction performance of these methods was ranked from highest to lowest as follows: Gradient Boosting Regression (GBM) > Random Forest (RF) > Adaptive Boosting (AdaBoost) > K-Nearest Neighbors (KNN) > Support Vector Regression (SVR). Among these algorithms, four out of the five achieved good prediction results, with the GBM method performing the best. The five most critical parameters influencing fatigue status were identified as working hours in protective clothing, a customized symptom and disease score (CSDS), physical exercise, body mass index (BMI), and age, all of which had importance scores exceeding 0.06. Notably, working hours in protective clothing obtained the highest importance score of 0.54, making it the most critical factor impacting fatigue status. Fatigue is a prevalent and pressing issue among medical security personnel operating in closed-loop environments. In our investigation, we observed that the GBM method exhibited superior predictive performance in determining the fatigue status of medical security personnel during the closed-loop period, surpassing other machine learning techniques. Notably, our analysis identified several critical factors influencing the fatigue status of medical security personnel, including the duration of working hours in protective clothing, CSDS, and engagement in physical exercise. These findings shed light on the multifaceted nature of fatigue among healthcare workers and emphasize the importance of considering various contributing factors. To effectively alleviate fatigue, prudent management of working hours for security personnel, along with minimizing the duration of wearing protective clothing, proves to be promising strategies. Furthermore, promoting regular physical exercise among medical security personnel can significantly impact fatigue reduction. Additionally, the exploration of medication interventions and the adoption of innovative protective clothing options present potential avenues for mitigating fatigue. The insights derived from this study offer valuable guidance to management personnel involved in organizing large-scale events, enabling them to make informed decisions and implement targeted interventions to address fatigue among medical security personnel. In our upcoming research, we will further expand the fatigue dataset while considering higher precisionprediction algorithms, such as XGBoost model, ensemble model, etc., and explore their potential contributions to our research.

Ecological stoichiometry of C, N, P and Si of Karst Masson pine forests: Insights for the forest management in southern China

Ecological stoichiometry is an effective method to study the stoichiometric relations and laws of elements in biogeochemical cycle, widely used in studies on nutrient cycles, limiting elements and nutrient utilization efficiency in ecosystems. To explore C, N, P, and Si stoichiometric characteristics and reveal these nutrient cycle processes and mechanisms in the karst Masson pine forests, the typical Masson pine forests of the three different stand ages in southern China were selected as the research objects and the C, N, P, and Si stoichiometric characteristics of soil-plant-litter continuum were studied. The followed results and conclusions were obtained: 1) Content range of TOC (total organic carbon), TN (total N), TP (Total P) and TSi (total Si) of the Masson pine forests was 288.31-334.61, 0.34-6.66, 0.11-1.05, and 0.76-11.4 g·kg-1, respectively. And the ratio range of C:N, C:P, C:Si, N:P, N:Si, and P:Si was 49.95-913.57, 99.98-2872.18, 22.48-429.31, 1.85-6.33, 0.17-6.01, and 0.04-0.91, respectively. 2) The significant differences in C, N, P, and Si stoichiometric characteristics were present between different organs or different forest ages. Leaves had the highest N and P content, while roots were the best enriched organ of Si element. Si content and C:Si were obviously correlated with forest age. 3) Significant N limitation was present in the Masson pine forests. And in the young and middle-aged forests, N limitation was more obvious. 4) The litter nutrients mainly came from branches. And the litter decomposed fast, which played an important role in the nutrient return of barren karst soil. The present results not only revealed the stoichiometric characteristics and cycling processes of C, N, P, and Si elements in the Masson pine forests, but also provided important scientific bases for the artificial management of Masson pine plantations in southern China.

Assessing and decoupling ecosystem services evolution in karst areas: A multi-model approach to support land management decision-making

Incorporating Ecosystem Service Value (ESV) into land use planning provides a fresh perspective for informed land management decisions. ESV, influenced by socio-economic and natural factors, has complex driving mechanisms, particularly in China's southwestern karst regions. Studying mediating variables helps elucidate these mechanisms. Further research into ecosystem services interactions and effective land use policies in karst areas is needed. This study evaluates the ESV of Guizhou Province, located in southern China's karst region, using the benefit transfer approach. Combining the Guizhou Provincial Land Use Planning Outline (2006-2020) with the multi-objective programming (MOP) model optimized by genetic algorithm and the patch-generating land use simulation (PLUS) model, four future development scenarios were designed. The response of ESV to land use and land cover (LULC) changes at the county scale under four different development scenarios from 2000 to 2020 and in the future was analyzed. A partial least squares structural equation model (PLS-SEM) was used to decouple the driving mechanism affecting ESV. The results show that over the past two decades, with the implementation of various ecological restoration projects, the total ESV has increased. The ESV for natural development scenarios, ecological conservation scenarios, economic development scenarios, and sustainable development scenarios are CNY 238.278 billion, CNY 400.514 billion, CNY 283.201 billion, and CNY 323.615 billion, respectively. The direct impacts of karst surface characteristic factors (KSCF), meteorological factors (MF), socio-economic factors (SEF) and transportation location factors (TLF) on ESV are positive (0.098), negative (-0.098), positive (0.336), and positive (0.109) respectively. The total effect of KSCF on ESV through influencing socio-economic factors and LULC is (-0.738), with SEF playing a complete mediating role. MF indirectly affect ESV by influencing LULC, with LULC playing a complete mediating role in this process. The PLS-SEM model shows that under the dominant position of LULC, the interaction between natural environmental factors and socio-economic factors on ESV is very complex. This study offers valuable insights that can guide managers in this region, as well as in other karst regions globally, in the development of sustainable land use policies.

Integrated microbiota and multi-omics analysis reveal the differential responses of earthworm to conventional and biodegradable microplastics in soil under biogas slurry irrigation

As one of the promising alternatives of conventional plastic mulching film (C-PMF), biodegradable plastic mulching films (B-PMF) were employed in agronomy production to alleviate the environmental burden of C-PMF. However, information regarding the potential toxicity effects of biodegradable microplastics (MPs) in soil still in scarcity, and the available findings were found to be controversial. Additionally, little is known about the molecular toxicity effects of conventional and biodegradable MPs on terrestrial organisms. Thus, 5 % (w/w) biodegradable (polylactic acid, PLA) and conventional (polyvinylchloride, PVC; low-density polyvinylchloride, LDPE) MPs were employed to assess the toxicity effects on Eisenia fetida in agricultural soil with biogas slurry irrigation. In the present study, transcriptomic, metabolomic profiles and individual indexes were selected to reveal the toxicity mechanisms from molecular level to the individual response. Furthermore, dysbiosis of bacterial community in gut was also investigated for obtaining comprehensive knowledge on the MPs toxicity. At the end of the exposure, the number of survival earthworms after MPs exposure was significantly reduced. Compared with the initial body weight, PLA and LDPE increased the biomass of earthworms after MPs exposure, while no significant influence on the biomass was observed in PVC treatment. Microbacterium, Klebsiella and Chryseobacterium were significantly enriched in earthworm gut after PLA, PVC and LDPE exposure, respectively (p < 0.05). Transcriptomic and metabolomic analysis revealed that PLA exposure induced neurotransmission disorder and high energetic expenditure in earthworms. However, PVC and LDPE inhibited the nutrient absorption efficiency and activated the innate immunity responses of earthworms. The PLS-SEM results showed that the effects of MPs were dominated by the polymer types, and hence, significantly and directly influence the gut bacterial community of earthworms. This study provides a better understanding of the similarities and discrepancies in toxicity effects of biodegradable and conventional MPs from the perspectives of individual, gut bacterial community, transcriptome and metabolome.

A comprehensive assessment of runoff dynamics in response to climate change and human activities in a typical karst watershed, southwest China

The Chengbi River Basin is a typical karst watershed in Southwest China. Understanding the effects of climate change (CC) and human activities (HAs) on hydrological process is important for regional water resources management and water security. However, a comprehensive assessment of the effects of CC and HAs on runoff dynamics at different time scales in the Chengbi River Basin is still lacking. To address these needs, we used Budyko Mezentsev-Choudhurdy-Yang and Slope change ratio of accumulative quantity methods to assess the contribution of the changing environment to annual and intra-annual runoff changes in the Chengbi River Basin. The results indicated that annual runoff time series was divided into the base phase Ta (1980-1996) and the change phase Tb (1997-2019). Compared to the natural status in Ta, the relative contributions of CC and HAs to the runoff increase in Tb were 154.86% and -54.86%. In addition, the shift in intra-annual runoff occurred in 2007 and was mainly caused by HAs, with a contribution rate of 76.22%. The increase in annual runoff in Tb could be attributed to the positive contribution of rainfall. Changes in rainfall and reservoir construction altered the original state of intra-annual runoff. Furthermore, the high degree of heterogeneity in the surface karst zone increased the runoff coefficient. The spatial unsaturation of the subsurface water-bearing media and rainfall patterns caused a significant lag effect in the response of surface runoff to rainfall. This study can help researchers and policy makers to better understand the response of karst runoff to changing environment and provide insights for future water resources management and flood control measures.