Group living in highland tuco-tucos (Ctenomys opimus) persists despite a catastrophic decline in population density.

Identifying the factors that favor group living is central to studies of animal social behavior. One demographic parameter that is expected to substantially shape spatial and social relationships is population density. Specifically, high population densities may favor group living by constraining opportunities to live alone. In contrast, low densities may allow individuals to spread out within the habitat, leading to a reduction in the prevalence or size of social groups. Abrupt changes in density following natural catastrophic events provide important opportunities to evaluate the effects of population density on patterns of spatial and social organization. As part of long-term studies of the behavioral ecology of a population of highland tuco-tucos (Ctenomys opimus) at Monumento Natural Laguna de los Pozuelos, Jujuy Province, Argentina, we monitored the demographic and behavioral consequences of a flood that inundated our study site during December 2012. Unlike most species of Ctenomys studied to date, highland tuco-tucos are group living, meaning that multiple adults share burrow systems and nest sites. Despite a post-flood reduction in population density of ~75%, animals present on the study site during the 2013 breeding season continued to live in multi-adult social units (groups). No differences between pre- and post-flood home range sizes were detected and although between-unit spatial overlap was reduced in 2013, overlap within social units did not differ from that in pre-flood years. Animals assigned to the same social unit in 2013 had not lived together during 2012, indicating that post-flood groups were not simply the remnants of those present prior to the flood. Collectively, these findings indicate that group living in highland tuco-tucos is not driven by the density of conspecifics in the habitat. In addition to enhancing understanding of the adaptive bases for group living in Ctenomys, our analyses underscore the power of catastrophic events to generate insights into fundamental aspects of social behavior.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Hydraulic modeling of the Periyar River using HEC-RAS: unraveling flow dynamics.

The Periyar River, a vital component of Kerala's ecosystem in India, serves as a lifeline supporting agriculture, hydropower generation, and ecological equilibrium. This study adopts a multifaceted approach to address critical challenges in the Periyar basin, with a primary focus on flood mitigation due to the region's susceptibility to devastating floods. Covering a length of 67.85 km, the study intricately segments the Periyar River into distinct reaches for a comprehensive steady flow analysis, considering factors such as seasonal monsoon fluctuations, diverse catchment topography, and human-induced alterations. Utilizing advanced modeling techniques, particularly HEC-RAS software, the study effectively predicts and simulates shifts in hydraulic behavior. The results, including velocity plots and cross-sectional maps, offer accurate insights into critical parameters, enabling the identification of areas with high velocity occurrence. This information proves instrumental in making informed decisions for the construction of river restoration structures, crucial for mitigating the impact of floods. The study's findings contribute valuable tools for future forecasting and sustainable management of the Periyar River, addressing the complex interplay of natural and anthropogenic factors.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Assessment of urban underground spaces inundation during extreme rainfall events.

As urbanization progresses and the impacts of climate change become more pronounced, urban flooding has emerged as a critical challenge for resilient cities, particularly concerning urban underground spaces where flooding can lead to significant loss of life and property. Drawing upon a comprehensive review of global research on underground space flood simulation and evacuation, this paper undertakes the modelling of inundation in a substantial underground area during the extraordinary rainfall event on 7 September 2023, in Shenzhen, China. Specifically, it introduces a two-step method to simulate the coupled surface-underground inundation process with high accuracy. The study simulates the inflow processes in three types of underground spaces: parking lots, metro stations, and underpasses. Utilizing the specific force per unit width evaluation, the research examines how varying flood barrier heights influence evacuation time and inundation risk. Subsequently, the paper proposes corresponding evacuation strategies based on the obtained findings. By highlighting the vulnerability of urban underground spaces to flooding, the study underscores the urgent need for further research in this domain.

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Green infrastructure layout based on a dynamic operation feature of drainage systems.

Increasingly frequent urban floods strain the traditional grey infrastructure, overwhelming the capacity of drainage networks and causing challenges in managing stormwater. The heavy precipitation leads to flooding and damage to drainage systems. Consequently, efficient mitigation strategies for flooding have been researched deeply. Green infrastructure (GI) has proved to be effective in responding the increasing risk of flood and alleviate pressure on drainage systems. However, as the primary infrastructure of stormwater management, there is still a lack of attention to the dynamic operation feature of urban sewer systems during precipitation events. To fill this gap, we proposed a novel approach that integrates hydraulic characteristics and the topological structure of a sewer network system. This approach aims to identify influential nodes, which contribute to the connectivity of the sewer network amidst dynamic changes in inflow during precipitation events. Furthermore, we adopted rain barrels to serve as exemplars of GI, and 14 GI layout schemes are produced based on the different ranks of influential nodes. Implementing GI measures on both poorly performing and well-performing nodes can yield distinct benefits in mitigating node flooding. This approach provides a new perspective for stormwater management, establishing effective synergy between GI and the drainage system.

Urban flood modeling with a novel coupling method of surface and sewer hydrodynamic processes.

Drainage modeling that accurately captures urban storm inundation serves as the foundation for flood warning and drainage scheduling. In this paper, we proposed a novel coupling ideology that, by integrating 2D-1D and 1D-2D unidirectional processes, overcomes the drawback of the conventional unidirectional coupling approach that fails to properly represent the rainfall surface catchment dynamics, and provides more coherent hydrological implications compared to the bidirectional coupling concept. This paper first referred to a laboratory experimental case from the literature, applied and analyzed the coupling scheme proposed in this paper and the bidirectional coupling scheme that has been widely studied in recent years, compared the two coupling solutions in terms of the resulting accuracy and applicability, and discussed their respective strengths and weaknesses to validate the reliability of the proposed method. The verified proposed coupling scheme was then applied to the modeling of a real drainage system in a region of Nanjing, China, and the results proved that the coupling mechanism proposed in this study is of practical application value.

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina.

Evaluating the forthcoming impacts of climate change is important for formulating efficient and flexible approaches to water resource management. General Circulation Models (GCMs) are primary tools that enable scientists to study both past and potential future climate changes, as well as their impacts on policies and actions. In this work, we quantify the future projected impacts of hydroclimatic extremes on the coastal, risk-prone Tar-Pamlico River basin in North Carolina using GCMs from the Sixth International Coupled Model Intercomparison Project (CMIP6). These models incorporate projected future societal development scenarios (Shared Socioeconomic Pathways, SSPs) as defined in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Specifically, we have utilized historical residential expansion data, the Soil and Water Assessment Tool Plus (SWAT+), the Standardized Precipitation Index (SPI), and the Interquartile Range (IQR) method for analyzing extremes from 2024 to 2100. Our findings include: (1) a trend toward wetter conditions is identified with an increase in flood events toward 2100; (2) projected increases in the severity of flood peaks are found, quantified by a rise of 21% compared to the 2000-2020 period; (3) downstream regions are forecast to experience severe droughts up to 2044; and (4) low-lying and coastal regions are found as particularly susceptible to higher flood peaks and more frequent drought events between 2045 and 2100. This work provides valuable insights into the anticipated shifts in natural disaster patterns and supports decision-makers and authorities in promoting adaptive strategies and sustainable policies to address challenges posed by future climate changes in the Tar-Pamlico region and throughout the state of North Carolina, United States.

A study on waterlogging tolerance in sugarcane: a comprehensive review.

Sugarcane (Saccharum officinarum) is an important crop, native to tropical and subtropical regions and it is a major source of sugar and Bioenergy in the world. Abiotic stress is defined as environmental conditions that reduce growth and yield below the optimum level. To tolerate these abiotic stresses, plants initiate several molecular, cellular, and physiological changes. These responses to abiotic stresses are dynamic and complex; they may be reversible or irreversible. Waterlogging is an abiotic stress phenomenon that drastically reduces the growth and survival of sugarcane, which leads to a 15-45% reduction in cane's yield. The extent of damage due to waterlogging depends on genotypes, environmental conditions, stage of development and duration of stress. An improved understanding of the physiological, biochemical, and molecular responses of sugarcane to waterlogging stress could help to develop new breeding strategies to sustain high yields against this situation. The present review offers a summary of recent findings on the adaptation of sugarcane to waterlogging stress in terms of growth and development, yield and quality, as well as biochemical and adaptive-molecular processes that may contribute to flooding tolerance.

[Effects of water level on soil seed bank in the floodplain wetland of Juzhang River, China]. / 水位变化对沮漳河河漫滩湿地土壤种子库的影响.

Aiming to understand the responses of soil seed bank to different water levels, we investigated vegetation and soil seed bank along a water level gradient (frequently flooded area, unflooded area) on the floodplain wetland of Juzhang River. We used the structural equation model to explore the direct and indirect effects of water level on soil seed bank, and used non-metric multidimensional scaling (NMDS) to assess the role of soil seed bank for vegetation regeneration. The results showed that the density of transient and persistent seed banks at unflooded area was 36.9% and 7.8% higher than that of frequently flooded area, respectively. Shannon index and Pielou index of seed bank and vegetation were significantly affected by water level and sampling location. Water level significantly affected the similarity between seed bank and aboveground vegetation, and the similarity of persistent seed bank with aboveground vegetation was significantly higher than that with transient seed bank. Structural equation model showed that water level had a direct effect on seed bank density, and indirect effects on density and richness of seed bank via affecting soil pH and NH4+-N content. NMDS results showed that there was no significant difference in the composition of the persistent seed bank and vegetation community in autumn under different water levels, but water level significantly changed the community composition of transient seed bank. Transient seed bank was affected by the vegetation and soil property, while persistent seed bank was determined by aboveground vegetation and water level. Although soil seed bank had low regeneration potential for the vegetation communities in floodplain wetlands, soil seed bank could not be neglected during the restoration of propagule diversity after disturbance in wetlands. Persistent seed bank would be an importance source of diversity of propagules for floodplain wetlands restoration following disturbance.

A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

UAV-based modelling of vegetation recovery under extreme habitat stresses in the water level fluctuation zone of the Three Gorges Reservoir, China.

Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contributions of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature periods, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing were found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by an unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.

Medical needs during the Kumamoto heavy rain 2020: analysis from emergency medical teams' responses.

BACKGROUND: Rainfall-induced floods represented 70% of the disasters in Japan from 1985 to 2018 and caused various health problems. To improve preparedness and preventive measures, more information is needed on the health problems caused by heavy rain. However, it has proven challenging to collect health data surrounding disasters due to various inhibiting factors such as environmental hazards and logistical constraints. In response to the Kumamoto Heavy Rain 2020, Emergency Medical Teams (EMTs) used J-SPEED (Japan-Surveillance in Post Extreme Emergencies and Disasters) as a daily reporting tool, collecting patient data and sending it to an EMTCC (EMT Coordination Cell) during the response. We performed a descriptive epidemiological analysis using J-SPEED data to better understand the health problems arising from the Kumamoto Heavy Rain 2020 in Japan. METHODS: During the Kumamoto Heavy Rain 2020 from July 5 to July 31, 2020, 79 EMTs used the J-SPEED form to submit daily reports to the EMTCC on the number and types of health problems they treated. We analyzed the 207 daily reports, categorizing the data by age, gender, and time period. RESULTS: Among the 816 reported consultations, women accounted for 51% and men accounted for 49%. The majority of patients were elderly (62.1%), followed by adults (32.8%), and children (5%). The most common health issues included treatment interruption (12.4%), hypertension (12.0%), wounds (10.8%), minor trauma (9.6%), and disaster-related stress symptoms (7.4%). Consultations followed six phases during the disaster response, with the highest occurrence during the hyperacute and acute phases. Directly disaster-related events comprised 13.9% of consultations, indirectly related events comprised 52.0%, and unrelated events comprised 34.0%. As the response phases progressed, the proportions of directly and indirectly related events decreased while that of unrelated events increased. CONCLUSION: By harnessing data captured by J-SPEED, this research demonstrates the feasibility of collecting, quantifying, and analyzing data using a uniform format. Comparison of the present findings with those of two previous analyses of J-SPEED data from other disaster scenarios that varied in time, location, and/or disaster type showcases the potential to use analysis of past experiences to advancing knowledge on disaster medicine and disaster public health.

Ammonium addition reduces phosphorus leaching in a long-term mineral or organic fertilized calcareous soil during flooding conditions.

Organic amendment substitutes mineral fertilizers has been proven to increase the organic matter content of soils, which in turn may induce phosphorus (P) mobilization by triggering the redox reaction. However, under flooded conditions according to local agricultural practices, as one of the factors restricting the decomposition of organic matter, the role ammonium plays in P transformation and leaching from soils with different organic matter remains unclear. To address the knowledge gap, the calcareous soils were collected from a long-term field trial (>13 years) containing two treatments with equal P inputs: a long-term mineral fertilization and a long-term organic amendment. Both long-term mineral fertilized soil and long-term organic amended soil were split into ammonium applications or no ammonium applications. A series of column devices were deployed to create flooded conditions and monitor the P leaching from the collected soils. The K-edge X-ray absorption near-edge structure and sequential extraction method were employed jointly to detect soil P fractions and speciation, and the P sorption/desorption characteristics of soil were evaluated by Langmuir fitting. The results showed a reduction of cumulative leached P from soils by 33.2%-43.3% after ammonium addition, regardless of previous long-term mineral fertilization or organic amendment history. A significant enhancement of soil labile P pool (indicated by the H2O-P fraction and NaHCO3-P fraction) after ammonium addition results in the reduction in soil P leaching. The reduced P sorption capacity coupled with the transformation from hydroxyapatite to ß-tricalcium phosphate indicated that the phosphate retention is attributed to the precipitation formation rather than phosphate sorption by soil. The present study highlights that the ammonium addition could affect the phosphate precipitation transformation. This may be attributed to the effect of ammonium addition on the calcium and magnesium ion content and molar ratio in this soil, thereby regulating the form of soil phosphate precipitation. The mechanisms revealed in this study can support developing optimized agricultural management practices to alleviate soil P loss.

Non-flooding conditions caused by water table drawdown alter microbial network complexity and decrease multifunctionality in alpine wetland soils.

Several soil functions of alpine wetland depend on microbial communities, including carbon storage and nutrient cycling, and soil microbes are highly sensitive to hydrological conditions. Wetland degradation is often accompanied by a decline in water table. With the water table drawdown, the effects of microbial network complexity on various soil functions remain insufficiently understood. In this research, we quantified soil multifunctionality of flooded and non-flooded sites in the Lalu Wetland on the Tibetan Plateau. We employed high-throughput sequencing to investigate the microbial community responses to water table depth changes, as well as the relationships between microbial network properties and soil multifunctionality. Our findings revealed a substantial reduction in soil multifunctionality at both surface and subsurface soil layers (0-20 cm and 20-40 cm) in non-flooded sites compared to flooded sites. The α-diversity of bacteria in the surface soil of non-flooded sites was significantly lower than that in flooded sites. Microbial network properties (including the number of nodes, number of edges, average degree, density, and modularity of co-occurrence networks) exhibited significant correlations with soil multifunctionality. This study underscores the adverse impact of non-flooded conditions resulting from water table drawdown on soil multifunctionality in alpine wetland soils, driven by alterations in microbial community structure. Additionally, we identified soil pH and moisture content as pivotal abiotic factors influencing soil multifunctionality, with microbial network complexity emerging as a valuable predictor of multifunctionality.

Sub1 QTL confers submergence tolerance in rice through nitro-oxidative regulation and phytohormonal signaling.

Constant change in global climate has become the most important limiting factor to crop productivity. Asymmetrical precipitations are causing recurrent flood events around the world. Submergence is one of the most detrimental abiotic stresses for sustainable rice production in the rainfed ecosystems of Southeast Asia. Therefore, the development of submergence-tolerant rice is an essential requirement to encounter food security. Submergence tolerance in rice is governed by the major quantitative trait locus (QTL) designated as Submergence1 (Sub1) near the centromere of chromosome 9. The introduction of the Sub1 in high-yielding rice varieties producing near-isogenic lines (NILs) has shown extreme submergence tolerance. The present study aimed to understand the responses of rice genotype IR64 and its Sub1 NIL IR64 Sub1 following one week of complete submergence treatment. Submergence imposed severe nitro-oxidative stress in both the rice genotypes, consequently disrupting the cellular redox homeostasis. In this study, IR64 exhibited higher NADPH oxidase activity accompanied by increased reactive oxygen species, reactive nitrogen species, and malondialdehyde buildups and cell death under submergence. Higher accumulations of 1-Aminocyclopropane-1-carboxylic acid, gibberellic acid, and Indole-3-acetic acid were also observed in IR64 which accelerated the plant growth and root cortical aerenchyma development following submergence. In contrast, IR64 Sub1 had enhanced submergence tolerance associated with an improved antioxidant defense system with sustainable morpho-physiological activities and restricted root aerenchyma formation. The comprehensive analyses of the responses of rice genotypes with contrasting submergence tolerance may demonstrate the intricacies of rice under complete submergence and may potentially contribute to improving stress resilience by advancing our understanding of the mechanisms of submergence tolerance in rice.

Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood-drought abrupt alternation.

BACKGROUND: Styrax tonkinensis (Pierre) Craib ex Hartwich faces challenges in expanding in the south provinces of Yangtze River region due to climate extremes like flood-drought abrupt alternation (FDAA) caused by global warming. Low tolerance to waterlogging and drought restricts its growth in this area. To study its antioxidant system and molecular response related to the peroxisome pathway under FDAA, we conducted experiments on two-year-old seedlings, measuring growth indexes, reactive oxygen species content, antioxidant enzyme activity, and analyzing transcriptomes under FDAA and drought (DT) conditions. RESULTS: The physiological results indicated a reduction in water content in roots, stems, and leaves under FDAA conditions. The most significant water loss, amounting to 15.53% was observed in the leaves. Also, ROS accumulation was predominantly observed in leaves rather than roots. Through transcriptome analysis, we assembled a total of 1,111,088 unigenes (with a total length of 1,111,628,179 bp). Generally, SOD1 and CAT genes in S. tonkinensis seedlings were up-regulated to scavenge ROS. Conversely, the MPV17 gene exhibited contrasting reaction with up-regulation in leaves and down-regulation in roots, leading to increased ROS accumulation in leaves. CHS and F3H were down-regulated, which did not play an essential role in scavenging ROS. Moreover, the down-regulation of PYL, CPK and CALM genes in leaves may not contribute to stomatal closure, thereby causing continuous water loss through transpiration. Whereas, the decreased root vigor during the waterlogging phase and up-regulated CPK and CALM in roots posed obstacles to water absorption by roots. Additionally, the DEGs related to energy metabolism, including LHCA and LHCB, were negatively regulated. CONCLUSIONS: The ROS generation triggered by MPV17 genes was not the main reason for the eventual mortality of the plant. Instead, plant mortality may be attributed to water loss during the waterlogging phase, decreased root water uptake capacity, and continued water loss during the subsequent drought period. This study establishes a scientific foundation for comprehending the morphological, physiological, and molecular facts of S. tonkinensis under FDAA conditions.

Biodegradable microplastics pose greater risks than conventional microplastics to soil properties, microbial community and plant growth, especially under flooded conditions.

Biodegradable plastics (bio-plastics) are often viewed as viable option for mitigating plastic pollution. Nevertheless, the information regarding the potential risks of microplastics (MPs) released from bio-plastics in soil, particularly in flooded soils, is lacking. Here, our objective was to investigate the effect of polylactic acid MPs (PLA-MPs) and polyethylene MPs (PE-MPs) on soil properties, microbial community and plant growth under both non-flooded and flooded conditions. Our results demonstrated that PLA-MPs dramatically increased soil labile carbon (C) content and altered its composition and chemodiversity. The enrichment of labile C stimulated microbial N immobilization, resulting in a depletion of soil mineral nitrogen (N). This specialized environment created by PLA-MPs further filtered out specific microbial species, resulting in a low diversity and simplified microbial community. PLA-MPs caused an increase in denitrifiers (Noviherbaspirillum and Clostridium sensu stricto) and a decrease in nitrifiers (Nitrospira, MND1, and Ellin6067), potentially exacerbating the mineral N deficiency. The mineral N deficit caused by PLA-MPs inhibited wheatgrass growth. Conversely, PE-MPs had less effect on soil ecosystems, including soil properties, microbial community and wheatgrass growth. Overall, our study emphasizes that PLA-MPs cause more adverse effect on the ecosystem than PE-MPs in the short term, and that flooded conditions exacerbate and prolong these adverse effects. These results offer valuable insights for evaluating the potential threats of bio-MPs in both uplands and wetlands.