A ridge-to-reef ecosystem microbial census reveals environmental reservoirs for animal and plant microbiomes.

Microbes are found in nearly every habitat and organism on the planet, where they are critical to host health, fitness, and metabolism. In most organisms, few microbes are inherited at birth; instead, acquiring microbiomes generally involves complicated interactions between the environment, hosts, and symbionts. Despite the criticality of microbiome acquisition, we know little about where hosts' microbes reside when not in or on hosts of interest. Because microbes span a continuum ranging from generalists associating with multiple hosts and habitats to specialists with narrower host ranges, identifying potential sources of microbial diversity that can contribute to the microbiomes of unrelated hosts is a gap in our understanding of microbiome assembly. Microbial dispersal attenuates with distance, so identifying sources and sinks requires data from microbiomes that are contemporary and near enough for potential microbial transmission. Here, we characterize microbiomes across adjacent terrestrial and aquatic hosts and habitats throughout an entire watershed, showing that the most species-poor microbiomes are partial subsets of the most species-rich and that microbiomes of plants and animals are nested within those of their environments. Furthermore, we show that the host and habitat range of a microbe within a single ecosystem predicts its global distribution, a relationship with implications for global microbial assembly processes. Thus, the tendency for microbes to occupy multiple habitats and unrelated hosts enables persistent microbiomes, even when host populations are disjunct. Our whole-watershed census demonstrates how a nested distribution of microbes, following the trophic hierarchies of hosts, can shape microbial acquisition.

Pathogenicity assessment of Arcobacter butzleri isolated from Canadian agricultural surface water.

BACKGROUND: Water is considered a source for the transmission of Arcobacter species to both humans and animals. This study was conducted to assess the prevalence, distribution, and pathogenicity of A. butzleri strains, which can potentially pose health risks to humans and animals. Cultures were isolated from surface waters of a mixed-use but predominately agricultural watershed in eastern Ontario, Canada. The detection of antimicrobial resistance (AMR) and virulence-associated genes (VAGs), as well as enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) assays were performed on 913 A. butzleri strains isolated from 11 agricultural sampling sites. RESULTS: All strains were resistant to one or more antimicrobial agents, with a high rate of resistance to clindamycin (99%) and chloramphenicol (77%), followed by azithromycin (48%) and nalidixic acid (49%). However, isolates showed a significantly (p < 0.05) high rate of susceptibility to tetracycline (1%), gentamycin (2%), ciprofloxacin (4%), and erythromycin (5%). Of the eight VAGs tested, ciaB, mviN, tlyA, and pldA were detected at high frequency (> 85%) compared to irgA (25%), hecB (19%), hecA (15%), and cj1349 (12%) genes. Co-occurrence analysis showed A. butzleri strains resistant to clindamycin, chloramphenicol, nalidixic acid, and azithromycin were positive for ciaB, tlyA, mviN and pldA VAGs. ERIC-PCR fingerprint analysis revealed high genetic similarity among strains isolated from three sites, and the genotypes were significantly associated with AMR and VAGs results, which highlight their potential environmental ubiquity and potential as pathogenic. CONCLUSIONS: The study results show that agricultural activities likely contribute to the contamination of A. butzleri in surface water. The findings underscore the importance of farm management practices in controlling the potential spread of A. butzleri and its associated health risks to humans and animals through contaminated water.

MRI in LARS1 deficiency-Spectrum, patterns, and correlation with acute neurological deterioration.

Leucine aminoacyl tRNA-synthetase 1 (LARS1)-deficiency (infantile liver failure syndrome type 1 (ILFS1)) has a multisystemic phenotype including fever-associated acute liver failure (ALF), chronic neurologic abnormalities, and encephalopathic episodes. In order to better characterize encephalopathic episodes and MRI changes, 35 cranial MRIs from 13 individuals with LARS1 deficiency were systematically assessed and neurological phenotype was analyzed. All individuals had developmental delay and 10/13 had seizures. Encephalopathic episodes in 8/13 were typically associated with infections, presented with seizures and reduced consciousness, mostly accompanied by hepatic dysfunction, and recovery in 17/19 episodes. Encephalopathy without hepatic dysfunction occurred in one individual after liver transplantation. On MRI, 5/7 individuals with MRI during acute encephalopathy had deep gray matter and brainstem changes. Supratentorial cortex involvement (6/13) and cerebellar watershed injury (4/13) occurred with seizures and/or encephalopathy. Abnormal brainstem contour on sagittal images (8/13), atrophy (8/13), and myelination delay (8/13) were not clearly associated with encephalopathy. The pattern of deep gray matter and brainstem changes are apparently characteristic of encephalopathy in LARS1-deficiency, differing from patterns of hepatic encephalopathy or metabolic stroke in organic acidurias and mitochondrial diseases. While the pathomechanism remains unclear, fever and energy deficit during infections might be causative; thus, sufficient glucose and protein intake along with pro-active fever management is suggested. As severe episodes were observed during influenza infections, we strongly recommend seasonal vaccination.

Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling.

Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export.

Wildfires Influence Mercury Transport, Methylation, and Bioaccumulation in Headwater Streams of the Pacific Northwest.

The increasing frequency and severity of wildfires are among the most visible impacts of climate change. However, the effects of wildfires on mercury (Hg) transformations and bioaccumulation in stream ecosystems are poorly understood. We sampled soils, water, sediment, in-stream leaf litter, periphyton, and aquatic invertebrates in 36 burned (one-year post fire) and 21 reference headwater streams across the northwestern U.S. to evaluate the effects of wildfire occurrence and severity on total Hg (THg) and methylmercury (MeHg) transport and bioaccumulation. Suspended particulate THg and MeHg concentrations were 89 and 178% greater in burned watersheds compared to unburned watersheds and increased with burn severity, likely associated with increased soil erosion. Concentrations of filter-passing THg were similar in burned and unburned watersheds, but filter-passing MeHg was 51% greater in burned watersheds, and suspended particles in burned watersheds were enriched in MeHg but not THg, suggesting higher MeHg production in burned watersheds. Among invertebrates, MeHg in grazers, filter-feeders, and collectors was 33, 48, and 251% greater in burned watersheds, respectively, but did not differ in shredders or predators. Thus, increasing wildfire frequency and severity may yield increased MeHg production, mobilization, and bioaccumulation in headwaters and increased transport of particulate THg and MeHg to downstream environments.

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.67 and 0.64, respectively) and use explainable artificial intelligence methods to interpret model predictions and understand salinization drivers. These models are applied to the Delaware River Basin, a developed watershed with diverse land uses that experiences anthropogenic salinization from winter deicer applications. These models capture seasonality for the winter first flush of deicers, and the streams with elevated predictions correspond well with indicators of deicer application. This result suggests that these models can be used to identify potential salinity-impaired streams for winter best management practices. Daily salinity predictions are driven primarily by land cover (urbanization) trends that may represent anthropogenic salinization processes and weather at time scales up to three months. Such modeling approaches are likely transferable to other watersheds and can be applied to further understand salinization risks and drivers.

Cervical anterior spinal artery infarction associated with anomalous vertebral artery: a case report.

We report a unique case of cervical anterior spinal artery (ASA) infarction in a 49-year-old male with hypercholesterolemia and sleep apnea. The patient experienced sudden cervical pain, quadriparesis, areflexia, and urinary incontinence after swallowing a large food bolus. Imaging revealed an infarction at the C3-C5 levels and an anomalous right vertebral artery (VA) originating from the thoracic aorta, tightly enclosed between the aorta and a vertebral column with an anterior osteophyte. This aberrant VA was the primary vascular supply to the ASA, with no contribution from the left VA or supreme intercostal arteries. We propose that transient injury to the right VA, induced by compression between the aortic arch, the food bolus, and the osteophyte, led to temporary hypoperfusion of the ASA, causing a watershed ischemic injury in the mid cervical cord's anterior gray matter. The article also provides an in-depth discussion of the developmental and clinical characteristics associated with this rare vascular anomaly.

Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia.

Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resource is now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN based AUC validation platform showed that, 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration, and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Evaluating and diagnosing ecosystem health of the "three-lake" watershed in Yuxi, Yunnan, China from 2010 to 2020 by PSR-KDE.

To carry out the diagnosis and evaluation of the ecosystem health in Yuxi three-lake watershed, this paper presents the changing trend of its health state, and predicts the future development. This also provides ideas for maintaining the regional ecosystem health, and then gradually improves the ecological environment quality. Taking Fuxian Lake, Qilu Lake and Xingyun Lake (the three-lake watershed) in Yuxi City, Yunnan Province, Southwest China as the research object, a model combining pressure-state-response and kernel density estimation (PSR-KDE) adopts to diagnose and evaluate the ecosystem health of the "three lake" watershed from 2010 to 2020, and the distribution map of ecosystem health index has obtained by the evaluation indexes integration based on GIS spatial analysis. Hence, the evaluation results have visualized on the map. The results show that: The distribution of ecosystem health index in the study area was 0.1530-0.7045 in 2010, 0.2056-0.7512 in 2015, and 0.2248-0.7662 in 2020. 0.12% was in the pathological area in 2010. After 2015, the pathological condition of ecosystem health has completely solved, and the proportion of unhealthy ecosystems was 11.95% in 2010, 7.38% in 2015, and 5.97% in 2020. The ecosystem health index of the study region was 0.5523 in 2010, 0.5807 in 2015, and 0.5815 in 2020, it indicates that the ecosystem was in a sub-health state. From 2010 to 2020, the ecosystem health around Qilu Lake was the most worrying, followed by the northwest of Fuxian Lake and the northern and southern regions of Xingyun Lake. The ecosystem health of the three-lake watershed showed significant improvement from 2010 to 2020. The study ecosystem health assessment and early warning in the three-lake watershed is significant to the ecological environment protection and management of the plateau lake basin, the restoration of the territorial space ecology and the economic development of the surrounding area.

A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco.

Water erosion poses a significant environmental threat in the Mediterranean region, with pronounced impacts observed throughout Morocco. It impairs soil quality and disrupts both sediment transport and water availability. Contributing factors range from natural (climate, topography, and geology) to anthropogenic (land use, vegetation cover, and management). This study introduces an improved Priority Actions Program/Regional Activity Centre (PAP/RAC) model, enriched with GIS and the Caesium-137 (137Cs) technique, to investigate erosion within Morocco's Raouz basin. Enhanced with additional variables including soil types, slope length, rainfall erosion potential, slope orientation, soil moisture, and land surface temperature, the model transcends the classical approach, promoting granularity and precision in predictions. In addition to the comprehensive model, the 137Cs method, which discerns long-term soil erosion and redistribution, provides a dual-faceted validation, bolstering the robustness of this project's erosion risk evaluation. This study's outcomes underscore the gravity of the erosion hazard with significant soil depletion rates ranging from 8.1 to 20 t ha-1 yr-1, demonstrating the model's alignment with empirical data, affirming its utility. The modified PAP/RAC model concurs with the 137Cs data, demonstrating its usefulness for water erosion assessment and management in similar areas.

Sediment properties control riverine methane emissions: A case study of the Liao river in northern China.

River and stream sediments act as biogeochemical reactors for greenhouse gases, particularly methane. However, understanding how riverbed sediment properties influence river carbon emissions remains relatively unclear. The Liao River in northern China is a typical watershed with heterogeneous water and sediment sources, characterized by varying sediment properties. In this study, we surveyed CH4 and CO2 emissions from its mainstem and tributaries during flood and dry seasons. We found consistent seasonal patterns in CH4 and CO2 emissions, with peaks occurring during the flood season. The average CH4 and CO2 fluxes were 1.64 ± 1.80 mmol m-2 d-1 and 59.66 ± 44.60 mmol m-2 d-1, respectively. Notably, the percentage of sediment silt was significantly correlated with CH4 concentration and flux (R2 = 0.12-0.30, p < 0.05). Fine particles dominated the availability of sediment organic matter and redox conditions, which were related to riverine CH4 production and emissions. Structural equation modeling revealed that both grain size and the percentage of TOC (total organic carbon) directly influenced riverine CH4 and CO2 emissions. The organic content and redox conditions of the riverbed sediment collectively explained 65% of riverine CH4 emissions, while grain size composition indirectly controlled CH4 emissions by altering sediment substrate quality and redox conditions. In contrast, river CO2 emissions were only weakly dependent on anaerobic metabolism in riverbed sediments. These findings enhance our understanding of the sources and metabolic mechanisms of riverine CH4 and CO2 emissions and offer potential improvements for estimating carbon fluxes in regional or global riverine networks by considering riverbed sediment properties.

Anthropogenic activities significantly interfered distribution and co-occurrence patterns of antibiotic resistance genes in a small rural watershed in Southwest China.

The prevalence and spread of antibiotic resistance genes (ARGs) have been a significant concern for global public health in recent years. Small rural watersheds are the smallest units of factor mobility for agricultural production in China, and their ARG profiles are the best scale of the contamination status, but the mapping and the distribution and diffusion of ARGs in the water and soil of small rural watersheds are inadequate. In this study, based on microbial metagenomics, we invested prevalence maps of 209 ARGs corresponding to typical commonly used antibiotics (including multidrug, aminoglycoside, macrolide-lincosamide-streptogramin B (MLSB), and ß-Lactamase) in water and soil in different agricultural types, as well as within water-soil interfaces in small rural watersheds in Southwest China. The results revealed that the most abundant ARGs in water and soil were consistent, but different in subtypes, and anthropogenic activities affect the transport of ARGs between water and soils. Livestock wastewater discharges influenced the diversity and abundance of ARGs in water, while in soil it is planting type and fertilizer management, and thus interfered with the co-occurrence patterns between bacteria and ARGs. Co-occurrence analysis revealed that Proteobacteria, Actinobacteria, and Bacteroidetes were the predominant ARG hosts in water and soil, but soil exhibited a more intricate ARG-bacterial association. Overall, this study provides integrated profiles of ARGs in water and soil influenced by anthropogenic activities at the small watershed scale in a typical rural area and provides a baseline for comparisons of ARGs.

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC.

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios-natural development, ecological protection, and arable land protection-between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

Effects of terrestrial dissolved organic matter on the growth, photosynthesis and colonial morphology of Microcystis aeruginosa at different levels of iron.

Terrestrial dissolved organic matter (tDOM) holds great promise for controlling cyanobacteria blooms through watershed management. To identify tDOM that could inhibit the growth, photosynthesis and colony formation, unicellular Microcystis aeruginosa Kützing (FACHB-469) was cultivated and treated with varying concentrations of gallic acid, proline and tea polyphenols at different levels of iron. The results indicated that gallic acid and tea polyphenols could inhibit Microcystis growth by suppressing photosynthesis and colony formation by reducing extracellular polysaccharides (EPS) secretion. However, proline had no significant effect on the growth, photosynthesis, colony size and EPS content of Microcystis. Transcriptome analysis showed Microcystis may optimize the internal energy transfer mode of photosynthesis through the change of phycobilisome at different levels of iron. In addition, Microcystis adapted to different iron concentration environments by regulating the expression of genes associated with iron uptake and transport. These findings suggest that the effects of plant species on algal blooms should be considered in reforestation of watershed. This consideration necessitates finding a balance between the costs and benefits of controlling cyanobacteria blooms using tDOM.

Bilateral watershed infarcts due to hypoperfusion in the context of drug abuse: case report.

BACKGROUND: Watershed infarcts (WIs) are a distinct type of stroke with a varying clinical presentation that affects the border areas between the territories of two cerebral arteries and are typically associated with hemodynamic impairment and internal carotid artery stenosis. However, there is a paucity of data concerning its association with the history of recreational substance and drug abuse. METHODS/CASE REPORT: This case report presents a unique instance of bilateral internal watershed infarcts in a 23-year-old male with a history of polysubstance abuse, including methadone and cocaine. The patient's presentation included confusion, lower limb weakness, and systemic complications such as acute liver injury and myonecrosis, underlying the complexity of the clinical scenario. RESULTS: The investigation revealed no evidence of arterial stenosis or thrombosis, leading to the conclusion that the infarctions were likely precipitated by a total loss of consciousness due to substance abuse-related cerebral hypoperfusion and vasoconstriction. Methadone and cocaine, both implicated in vasoconstriction, lowering the seizure threshold and contributing to QTc prolongation, thus leading to loss of consciousness, were identified as potential triggers for the episode. CONCLUSIONS: In the young adult population, it is important to consider drug abuse as an etiological trigger for watershed infarcts, whereas the multi-system involvement and atypical presentation highlight the need for a comprehensive approach.

Terrestrial vegetation carbon sink analysis and driving mechanism identification in the Qinghai-Tibet Plateau.

The estimation of terrestrial carbon sinks in the Qinghai-Tibet Plateau (QTP) still faces significant uncertainties, and the spatiotemporal dynamics of terrestrial carbon sinks along altitudinal gradients remain unexplored. Moreover, the driving mechanisms of terrestrial carbon sinks at the watershed scale in the QTP continue to be lacking. To address these research gaps, based on multi-source remote sensing data and meteorological data, this study calculated the Net Ecosystem Productivity (NEP) in the QTP from 2000 to 2020 using the Modis NPP-soil respiration model. Through the coefficient of variation (CV), the Mann-Kendall test (MK), and the spatial autocorrelation methods, the spatial distribution pattern and spatiotemporal trends of NEP were investigated. Employing a pixel accumulation method, the variation of NEP along altitudinal gradients was explored. Grey relation analysis, Pearson correlation analysis, and Geographical detector (GD) were used to investigate the driving mechanisms of NEP at the watershed scale. Results showed that: (1) the terrestrial ecosystem in the QTP served as a carbon sink, which produced a total of 2.04 Pg C from 2000 to 2020, and the multi-year average of total carbon sinks was 96.92 Tg C; (2) the spatial distribution of NEP shows a decreasing change from southeast to northwest, and the clustering characteristic of NEP is significant at the watershed scale; (3) the elevation of 4507 m we proposed is likely to be a key threshold for biophysical processes of the terrestrial ecosystems in the QTP; (4) the fluctuation and change trend of carbon sources and carbon sinks show significant differences between the East and West; (5) at the watershed scale, precipitation and temperature play a dominant role in the variation of NEP, while the impact of human activities on NEP variation is weak. Our study aims to address the existing knowledge gaps and provide valuable insights into the management of terrestrial carbon sinks in QTP.

Biogenic elements-informed assessment of the impact of human activities on river ecosystems.

River ecosystems, acting as pivotal conduits linking terrestrial, marine, and atmospheric realms, have faced significant disturbances due to human exploitation of their resources. Recent years have witnessed a heightened intensification of human activities, adversely affecting the equilibrium of water ecosystems. To systematically study the various factors that affect river ecosystems under human activities, we introduce a universally applicable approach that considers the diversity of watersheds, biogenic elements, and human activities. Using this method, this application uncovers the sensitive human activity types, biogenic factors, and species significantly influencing river biodiversity within the study area. Incorporating statistical modelling, sensitivity screening, and advanced correlation analyses within a random forest regression framework, Sensitive biogenic elements and biological types affected by human activities were identified in typical watersheds, and the stability of different aquatic ecosystems was evaluated. Suggestions for watershed management measures were proposed When human activities affect the degree of water resource development and utilization, the forms of sensitive biogenic elements include DIC and Tsi; When human activities affect the discharge of pollutants into rivers, the forms of sensitive biogenic elements include TP, PP, and DEP, and the ratio composition includes TC: TN, TC: TP, TP: TSi, and TN: TP, This study pioneers a novel method for assessing human impacts on river ecosystems and successfully applies this approach to inform management decisions for river segments and tributaries in the middle and upper reaches of the Yangtze River basin. thereby enhancing our understanding of the consequences of human-induced impacts on biodiversity.

Dual stable isotopes to rethink the watershed-scale spatiotemporal interaction between surface water and groundwater.

The interaction between groundwater and surface water, including their recharge relationship and ratio, is crucial for water cycling, management, and pollution control. However, accurately estimating their spatiotemporal interaction at the watershed scale remains challenging. In this study, we used dual stable isotopes (δ18O, δ2H, d-excess, and lc-excess) and hydrochemistry methods to rethink spatiotemporal interaction at the Yiluo River watershed in central China. We collected 20 groundwater and 40 surface water samples over four periods in two seasons (dry and wet). Our results showed that in the downstream region, groundwater recharged surface water in the dry season while surface water recharged groundwater in the wet season, with average recharge ratios of 89.82% and 90.02%, respectively. In the midstream region, surface water recharged groundwater in both seasons with average ratios of 93.79% and 91.35%. In contrast, in the upstream region, groundwater recharged surface water in both seasons with ratios of 67.35% and 76.89%. Seasonal changes in the recharge relationship between surface water and groundwater in the downstream region also been found. Our findings provide valuable insights for watershed-scale water resource and pollution management.

How can the microbial community in watershed sediment maintain its resistance in the presence of shifting antibiotic residuals?

The widespread presence of antibiotics in global watershed environments poses a serious threat to public health and ecosystems. It is essential to examine the resistance of microbial communities in watershed environments in response to shifting antibiotic residues. Sediment samples were collected from seven sites across a watershed, encompassing surface sediment (0-10 cm) and bottom sediment (30-40 cm) depths. The aim was to replicate exposure scenarios to different antibiotics (oxytetracycline (OTC) and sulfadiazine (SD)) at varying concentrations (0, 10, and 100 µg/L) in sediment overlying water, within controlled laboratory settings. The study findings revealed significant variations in the microbial community structure of sediments between different treatments, with distinct differences observed in the upper stream and top sediment layers compared to the sediments located downstream and in the bottom layers. After the introduction of antibiotics, a significant decrease in microbial nodes was observed in the genus-level co-occurrence network analysis of the bottom sediment layer, particularly in the OTC treatment groups. In contrast, the downstream region displayed more robust correlations among the top 20 genera than the upstream area. There was no significant variance observed in the expression of Antibiotic resistance genes (ARGs), consisting of tetracycline resistance genes (tetC, tetG, tetM, tetW, and tetX) and sulfonamide resistance genes (sul1, sul2, and sul3), between sediments in the top and bottom layers. Nevertheless, downstream samples exhibited significantly higher levels of ARGs when compared to upstream samples. Network correlation analysis indicated notably lower correlations between ARGs and bacterial genera in sediments from upstream or surface layers compared to those in downstream or deeper layers. Moreover, correlations in the sediments from surface layers and upstream regions showed a decreasing trend with increasing SD exposure concentrations, while those in deeper layers and downstream areas remained relatively stable. The presence of antibiotics notably enhanced the correlation between sediment properties and ARGs, particularly emphasizing associations with total carbon, nitrogen, and sulfur content. However, the introduction of SD and OTC resulted in a decrease in the influence of these sediment factors on microbial community functions related to sulfur and nitrogen metabolism, as indicated by KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation. The research provided empirical evidence on how microbial resistance responds to changes in antibiotics in sediment samples taken from various depths and locations within a watershed. It emphasized the urgent need for heightened awareness of the movement and alteration of antibiotic resistance patterns in watershed ecosystems.

Assessment of the viral contamination of fecal origin over a wide geographical area using an active approach with Dreissena polymorpha.

Biomonitoring appears to be a key approach to assess chemical or microbiological contaminations. The freshwater mussel, Dreissena polymorpha (D. polymorpha), is a suitable tool already used to monitor chemical and, more recently, microbiological pollution. In the present study, we used this sentinel species to monitor viral contamination of fecal origin over a wide geographical distribution. An active approach was implemented based on caging of calibrated and pathogen-free organisms with the same exposure conditions, allowing spatio-temporal comparisons between different water bodies. In addition, different types of sites were selected to investigate the range of environmental concentrations that D. polymorpha are able to translate. Different viral genome targets were measured: norovirus genogroup I and II (NoV GI and GII) and F-specific RNA bacteriophages belonging to the genogroup -I and -II (FRNAPH-I and -II). Total infectious FRNAPH were also monitored. D. polymorpha was able to translate a wide range of concentrations for all the viral targets studied, meaning that this sentinel species can be used for both low and highly anthropised sites. Moreover, D. polymorpha caging proved effective in achieving gradients of viral contamination of fecal origin pressure and to highlight the contribution of tributaries to the main rivers. D. polymorpha provided spatial and temporal variations of the viral contamination. It allowed to highlight the prevalence of the FRNAPH-I and -II genogroups according to the caging site. FRNAPH-II was found to be dominant in urban areas and FRNAPH-I in rural areas. This strategy uses the caging of the sentinel species D. polymorpha on selected sites with standardised analysis methods has proven to be a promising tool for characterizing viral contamination at both large and very fine scales.