Characteristics Of Water Bodies Research Articles

Potential associations between Schistosoma mansoni infection and physico-chemical characteristics and water-related human activities in Côte d'Ivoire: a cross-sectional study.

Schistosomiasis remains a public health problem, particularly in sub-Saharan Africa. The disease is intimately connected to poverty and environmental factors. Our research was readily embedded into a multi-country schistosomiasis oversampling study. The aim of the study presented here was to determine the prevalence of Schistosoma mansoni and to investigate the role of water body characteristics and water-related human activities in disease transmission. In August and September 2022, a cross-sectional study was conducted in the western part of Côte d'Ivoire. Stool and urine samples were collected from 1602 and 1729 children aged 5-14years, respectively, in 65 villages in the health districts of Biankouma, Ouaninou and Touba. Additionally, data were collected from direct observation of water-related activities at water bodies and interviews conducted with community leaders and health workers. The prevalence and risk factors for Schistosoma infection were assessed using generalised estimating equation models. The prevalence ofS.mansoni and S.haematobium were 27.4% (95% confidence interval [CI] 21.5-34.3%) and 0.1% (95% CI 0.03-0.5%), respectively. Low prevalence of soil-transmitted helminths was observed with 2.4%, 0.4% and 0.2% for hookworm, Trichuris trichiura and Ascaris lumbricoides, respectively. At the health district level, we found S.mansoni prevalence of 34.4% (95% CI 25.0-45.3%), 34.3% (95% CI 24.0-46.2%) and 16.3% (95% CI 9.5-26.6%) for Biankouma, Ouaninou and Touba, respectively. Female and male participants were at a similar risk of infection (29.0% vs. 26.0%, odds ratio [OR]: 1.18, 95% CI 0.92-1.50). Children aged 9-14years showed a higher prevalence than their younger counterparts aged 5-8years (34.5% vs. 22.7%, OR: 1.80, 95% CI 1.42-2.27). High infection prevalence was observed in villages where children were washing clothes and dishes at open surface water sites and pursued recreational activities (e.g. swimming and playing in the water). The temperature, total dissolved solids and pH of water samples showed no significant association with S.mansoni infection at the village unit. Human water-related activities such as washing clothes and playing in the water are risk factors for S.mansoni transmission. Hence, preventive chemotherapy should be combined with information, education and communication to avoid or reduce the frequency of water exposure in children as part of a comprehensive package of interventions towards elimination of schistosomiasis as a public health problem.

Habitat requirements of Luronium natans (L.) Raf. in northern Belgium

We investigated the environmental range of Luronium natans populations in lower Belgium (Flanders) with particular focus on key factors influencing its population size and the interplay of nutrient availability and physical disturbances in determining its abundance. Soil calcium content and total phosphorus in the water layer were the only physical-chemical variables related to L. natans abundance. Physical water body and vegetation characteristics influenced population dynamics and incidence of reproductive strategies. The species has a ruderal character, and occurs early in vegetation succession or depends on regular physical disturbance events for prolonged presence. Presenting limited competitive abilities, especially in terms of light availability, the species is hindered by taller-growing vegetation. Large populations are typically associated with well-lit conditions. Its flexibility to switch between vegetative and generative reproduction strategies may be associated with nutrient and light conditions. The need for long-term monitoring to consider population dynamics and evaluate the effectiveness of conservation efforts is emphasized. Overall, our study provides further insight into the relation between abiotic conditions, population dynamics and conservation management and extends the basis for sustainable management of the remaining L. natans populations in Flanders and similar regions of Atlantic Europe.

Hydrogeochemical evolution patterns of diverse water bodies in mining area driven by large-scale open-pit combined underground mining-taking Pingshuo Mining Area as an example

Large-scale open-pit combined underground mining activities (OUM) not only reshape the original topography, geomorphology, and hydrogeochemical environment of the mining area, but also alter the regional water cycle conditions. However, due to the complexity arising from the coexistence of two coal mining technologies (open-pit and underground mining), the hydrological environmental effects remain unclear. Here, we selected the Pingshuo Mining Area in China, one of the most modernized open-pit combined underground mining regions, as the focus of our research. We comprehensively employed mathematical statistics, Piper diagram, Gibbs model, ion combination ratio, principal component analysis and other methods to compare the hydrochemistry and isotope data of different water bodies before (2006) and after (2021) large-scale mining. The changing patterns of hydrochemical characteristics of different water bodies and their main controlling factors in mining area driven by OUM were analyzed and identified, revealing the water circulation mechanism under the background of long-term coal mining. The results showed that: (1) The chemical composition of water has changed greatly due to large-scale coal mining. The hydrochemical types of Quaternary and Permian-Carboniferous aquifers shifted from predominantly HCO3-Ca·Mg before intensive mining to primarily HCO3·SO4-Ca·Mg, HCO3-Na, HCO3·SO4-Na·Mg, and HCO3·SO4-Ca·Mg, HCO3-Ca·Na, HCO3·SO4-Mg·Ca post-mining. Variations in the hydrochemical types of surface water were found to be complex and diverse. (2) Coal mining activities promote the dissolution of silicate rock and sodium-bearing evaporites, enhancing the strength and scale of positive alternating adsorption of cations. The oxidation of pyrite, dissolution of silicate weathering, and the leaching of coal gangue were identified as the main reasons for the significant increase of SO42−, while decarbonation in confined aquifers led to a decrease in HCO3−. (3) Results from the principal component analysis and stable isotopes demonstrated the hydraulic connection among surface water, Quaternary aquifers, and Permian-Carboniferous aquifers induced by long-term OUM. The research findings provide a reference basis for the coordinated development of coal and water in the Pingshuo Mining Area and other open-pit combined underground mining areas.

Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection

Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation of flood inundation extent during the rainy period. Post-classification comparison is one of the fundamental methods for flood mapping of multi-source imagery, as the image characteristics of SAR and optical imagery are inherently different. However, the accuracy of flood mapping depends on the accuracy of water body delineation from single temporal imagery, which can lead to error propagation in flood extent determination. Change detection methods can extract flood extent directly from pre- and post-event imagery, but the models need to learn a consistent flood feature description between optical and SAR imagery from a limited flood dataset. Here, we propose an automatic method for flood inundation extent extraction using pre- and post-event multi-source imagery that requires only a small number of pseudo-change labels. The methodology adopts a pseudo-Siamese network framework as a Heterogeneous - Flood Inundation Extraction Network (H-FIENet), to detect flood extent between the pre‐ and post-event heterogeneous images. Spatially inconsistent multi-source pre- and post-event imagery was used to create a pseudo flood dataset, and this dataset was used to transfer water body feature knowledge from the pre-trained model to the flood extraction model using a cross-task knowledge transfer strategy. Pre- and post-event images from different satellite sources and different flood scenarios were used to evaluate the performance of H-FIENet. We found that: (1) The methodology produced an overall accuracy of over 0.94 for flood inundation extraction from multi-source heterogeneous pre- and post-event imagery. (2) H-FIENet can detect both expansion and recession of the flood extent using any dual-temporal imagery. Our work makes it possible to automate time-series flood monitoring from multi-source remote sensing imagery.

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

Quantification and analysis study for the morphological characteristics of urban waterbodies: Evidences from 42 cases in China

Influenced by the water design principle of Chinese classical garden, the design of urban waterbodies (lakes, ponds and wetlands) in China typically exhibits natural-like morphological characteristics, but what similarities and differences among these characteristics remain unclear. Also, the morphology of urban waterbodies is mostly designed with beauty and lacks quantitative research and methodological support, which is not favourable to the exploration of the correlation between waterbody morphology and ecology. In this study, 11 quantifiable morphological indicators are selected from water surface, shoreline complexity and water-land spatial morphology. A Grasshopper-based model for waterbody morphology quantification and analysis was constructed and applied to 42 cases in China. Combined with frequency, correlation and PCA analysis, the cases and indicators with higher composite scores generally showed more natural water surface morphology, island distribution, and diversified land and water spaces. The thresholds for the more representative indicators are 0.10–0.60 for the circularity ratio (C), 1.01–1.15 for the shoreline fractal dimension (D0), 1.58–29.42 for the island nearest-neighbour index (NN), 1.20–2.50 for the shoreline development index (SDI), 0.20–0.55 for the compactness ratio (Rc) and 0.30–0.85 for the unit water surface morphological difference ratio (ΔB). Focusing on these indicators, along with ΔB and I which describe spatial inclusiveness and island distribution, their regression equations and threshold ranges provide a primary basis for developing waterbody morphology designs and generative algorithms. This methodology has been experimentally applied in the design of a case waterbody in Suqian City Park. Overall, this study constructs and validates the feasibility of a Grasshopper-based algorithm for the quantitative analysis and morphogenetic design of waterbody morphology. It demonstrates the potential of this method for understanding complex waterbody morphology and water-land spaces, and supports a new digital methodology for the optimisation of waterscape design.

Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing

Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry), a data filtering procedure designed to enhance satellite altimetry retrievals. MoRLa increases the acceptance of satellite observations and improves the quality of water level estimates by using physical characteristics of water bodies to exclude non-conforming measurements. Unlike previous studies with static masks, MoRLa employs a dynamic filter adaptable to actual water levels at specific times. Tested on reservoirs in the Korean Peninsula, including the Hwang-Gang dam, MoRLa shows significant improvements in water level measurements using Cryosat-2, ICESat-2, and Sentinel-3A and B satellites.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Understanding the spatial and temporal distribution of small water bodies is essential for managing water resources, crafting conservation policies, and preserving watershed ecosystems and biodiversity. However, existing studies often rely on a single remote sensing data source (optical or microwave), focusing on large-scale, flat areas and lacking comprehensive monitoring of small water bodies in complex terrain. Therefore, considering the complementary advantages of multisource remote sensing (multispectral and SAR), this paper proposes a multispectral and SAR fusion algorithm, named Multispectral and SAR Fusion algorithm (MASF), to better capture the remote sensing characteristics of small water bodies in complex areas. Based on this, a dataset containing spectral, texture, and geometric features is constructed, and multi-scale segmentation and random forest algorithms are applied for identification of small water bodies in complex terrain. The results demonstrate that the proposed fusion algorithm MASF exhibits minimal spectral distortion (SAM < 3.5, ERGAS <21, RMSE <0.01) and robust spatial feature enhancement (PSNR >40, SSIM >0.999, CC > 0.99). The Overall Accuracy (OA) and Kappa coefficients for both experimental areas surpassed 0.9. For rivers and reservoirs, both Producer's Accuracy (PA) and User's Accuracy (UA) exceeded 0.9. The UA for agricultural ponds exceeded 0.8. Comparative analysis with three other types of water-related data products shows that the freshwater identification results in this study have certain advantages in local small water bodies. Our research holds significant implications for the utilization of water resources in mountainous areas, prevention and control of floods and floods, as well as the development of aquaculture industry.

Habitat features important for the conservation of the endangered Sloane’s Froglet (Crinia sloanei) in peri-urban environments

Context Determining and quantifying habitat selection of endangered species in peri-urban environments assists planners and managers to develop strategies and alternative conservation measures in the face of urban expansion and development. Sloane’s Froglet (Crinia sloanei), listed nationally as endangered in Australia, is a little-known species distributed within peri-urban environments, where foundational ecological information and the development of adequate conservation responses has been lacking. Aims (a) To determine a core calling period for Sloane’s Froglet and detection probabilities for occupancy surveys. (b) To understand and characterise the habitat that Sloane’s Froglet uses at the wetland and microhabitat scale. Methods We used generalised linear modelling and the information-theoretic approach to model habitat preferences for this species at two scales: the waterbody scale, and the microhabitat scale. We quantified the habitat characteristics of waterbodies occupied by Sloane’s Froglet in winter, its peak breeding period, by measuring the biophysical characteristics of 54 occupied and 40 unoccupied waterbodies. The microhabitat and relative spatial positioning of Sloane’s Froglet within waterbodies was examined at 54 calling sites in an area of one m squared around individual male Sloane’s Froglets and 57 randomly selected unused sites. Wetlands were surveyed multiple times to determine detection probabilities. Key results Model selection indicated that Sloane’s Froglet is 450 times more likely to occupy a waterbody when an adjacent ephemeral shallow overflow is present; and are more likely to be present when there is greater cover of small stem-diameter emergent vegetation and less bare ground on the bank. The microhabitat investigation of one m squared sites showed that Sloane’s Froglet’s calling sites are predominantly inundated, and at significantly shallower water depths, than unused sites. Sloane’s Froglet was found to always call from within the waterbody, distinguishing them from other sympatric Crinia species. Conclusions The habitat characteristics detailed provide information necessary for the management of Sloane’s Froglet and its habitat. Implications Housing and industrial development is occurring rapidly in Sloane’s Froglet habitat. The information provided here can be used to refine local and state government planning and better design appropriate responses. Indeed, results from this study are currently being used by agencies and environmental consultants when developing conservation plans and in the design of stormwater retention ponds in rapidly urbanising environments.

Hydrochemical and Stable Isotopic Characteristics and Transformation Relationship of Water Bodies in the Guohe River Basin, Anhui Province

The Guohe River Basin in Anhui Province was selected as the research area for this study. By collecting surface water, shallow groundwater, and middle-deep groundwater samples, various hydrochemical parameters and stable isotopes of water in different water bodies were analyzed using methods such as the Gibbs diagram, ion ratios, and MixSIAR model to reveal and quantify the transformation relationships between these water bodies. The results indicated that both surface water and groundwater in the study area were predominantly neutral to weakly alkaline. The hydrochemical types of surface water were mainly characterized by Cl·SO4·HCO3-Na and Cl·SO4-Na types, whereas the shallow groundwater exhibited HCO3-Ca·Mg and HCO3-Mg·Na types, and the middle-deep groundwater was of the Cl·HCO3-Na type. The hydrochemical characteristics of various water bodies were influenced by multiple factors such as rock weathering, evaporation concentration, and positive cation exchange. The distribution characteristics of δ18O and δ2H values in surface water and groundwater indicated that atmospheric precipitation was the main water source. The δ18O and δ2H in groundwater were significantly correlated with K+, Na+, Cl-, SO42-, and NO3-. According to the analysis using the MixSIAR model, the contribution of atmospheric precipitation to surface water was 46.5 %, whereas the contribution from shallow groundwater was 53.5 %. The sources of shallow groundwater were identified as atmospheric precipitation (57.4 %) and surface water (42.6 %), and the main source of supply for middle-deep groundwater was lateral flow from upstream groundwater.

Acid mine drainage from coal mines in the eastern Himalayan sub-region: Hydrogeochemical processes, seasonal variations and insights from hydrogen and oxygen stable isotopes

Uncontrolled coal mining using non-scientific methods has presented a major threat to the quality of environment, particularly the water resources in eastern himalayan sub-region of India. Water bodies in the vicinity of mining areas are contaminated by acid mine drainage (AMD) that is released into streams and rivers. This study attempted to assess the impact of AMD, deciphering hydrogeochemical processes, seasonal fluctuations, and stable isotope features of water bodies flowing through and around coal mining areas. Self-organizing maps (SOMs) used to separate and categorize AMD, AMD-impacted and non-AMD impacted water from the different study locations for two sampling seasons revealed four clusters (C), with C1 and C2 impacted by AMD, C3 and C4 showing negligible to no impact of AMD. AMD impacted water was SO42− - Mg2+- Ca2+ hydrochemical type with sulphide oxidation and evaporation dominating water chemistry, followed by silicate weathering during both the sampling seasons. Water with negligible-to-no AMD-impact was Mg2+- Ca2+- SO42− to Ca2+ - HCO3− to mixed hydrochemical type with rock weathering and dissolution, followed by ion exchange as major factors controlling water chemistry during both the sampling seasons. Most of physicochemical parameters of C1 and C2 exceeded the prescribed limits, whereas in C3 and C4 water samples, parameters were found within the prescribed limits. Stable isotopes of hydrogen (δ2H) and oxygen (δ18O) during post-monsoon (PoM) varied between -41.04 ‰ and -29.98 ‰, and −6.60 ‰ to −3.94 ‰; and during pre-monsoon (PrM) varied between −58.18 ‰ and - 33.76 ‰ and −8.60 ‰ to −5.46 ‰. Deuterium excess (d-excess) ranged between 1.57 ‰ and 12.47 ‰ during PoM and 5.70 ‰ to 15.17 ‰ during PrM season. The stable isotopes analysis revealed that evaporation, mineral dissolution and mixing with rainwater are the key factors in study area.

A high-resolution dataset of water bodies distribution over the Tibetan Plateau

Water body (WB) extraction is the basic work of water resources management. Tibetan Plateau is one of the largest alpine lake systems in the world. However, research on the characteristics of water bodies (WBs) is mainly focused on large and medium WBs due to spatial resolution. This research presents a dataset containing a 2-m resolution map of WBs in 2020 based on Gaofen-1 data, and morphometric and landscape indices of WBs across the Tibetan Plateau. The Swin-UNet model is well performed with overall accuracy at 98%. The total area of WBs is 56354.6 km2 across Tibetan Plateau in 2020. The abundance compared with that from size-abundance relationship indicate WBs in the Tibetan Plateau conformed to the classic power scaling law. We evaluate the influence of spatial-resolution in WB extraction, which shows the dataset could be valuable to fill the gap of existing WBs map, especially for small waters. The dataset is valuable for revealing the spatial patterns of WBs, and understanding the impacts of climate change on water resources in Plateau.

Environmental Monitoring in Internet of Things (IOT)

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.

The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing

Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalized Difference Water Index (NDWI). Furthermore, aiming at the common water shadow problem in urban hyperspectral remote sensing images, the advantages of the PCA-NDWI model were further verified by experiments. By comparing the accuracy and F1-Measure of the PCA-NDWI, NDWI, HDWI, and K-means models, the results demonstrated that the PCA-NDWI model was better than the other tested methods. The accuracy and F1-Measure of the PCA-NDWI model water extraction data were 0.953 and 0.912, respectively, and the accuracy and F1-Measure of the PCA-NDWI model water shadow extraction data were 0.858 and 0.872, respectively. Therefore, the PCA-NDWI model can effectively separate shadows and the surrounding features of urban water bodies, accurately extract water body information, and has great application potential in water resources management.

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%.

Predation risk-mediated indirect effects promote submerged plant growth: Implications for lake restoration

Biological manipulation, involving fish stockings, is commonly used to counteract the deterioration of submerged vegetation in eutrophic lakes. Nevertheless, the non-consumptive effects (NCEs) of stocked carnivorous fish are often overlooked. Using a controlled experimental system, we investigated the NCEs of a native carnivorous fish, snakehead (Channa argus), on two key biological factors, herbivore-dominated grass carp (Ctenopharyngodon idella) and disturbance-dominated loach (Misgurnus anguillicaudatus), influencing submerged plants growth. Additionally, we conducted a meta-analysis on predation risk and primary productivity. The results reveal that predation risk induces oxidative stress damage and affects grass carp growth. Non-significant changes in cortisol and glucose may be linked to predation risk prediction. Simultaneously, predation risk reduces fish feeding and disturbance behavior, relieving pressure on submerged plants to be grazed and disturbed, thereby supporting plant development. The presence of submerged plants, in turn, enhances loach activity and influences water body characteristics through negative feedback. Furthermore, the meta-analysis results indicate the facilitative effect of predation risk on primary producers. Our findings contribute to the understanding of biological manipulation theory. We demonstrate that the predation risk associated with introducing carnivorous fish can promote the growth of submerged plants through behaviorally mediated indirect effects. This highlights the potential utility of predation risk in lake restoration efforts.

Influence of the functional and morphological features of floodplain water bodies on the indicators of water quality

ABSTRACT The construction of dams on large rivers significantly changes the hydrological regime. The morphology and functional features of floodplain water bodies have a significant impact on water quality parameters. The electrical conductivity, transparency, water temperature and dissolved oxygen content of 302 water bodies located in the Dnipro River floodplain were investigated. The variability of water quality indicators is explained by different sources of water supply to the floodplain reservoirs, as well as by the geological characteristics of the soil. The genesis of lakes of a certain shape is related to the geological and geomorphological conditions of the floodplain, which is the reason for the connection between the functional characteristics of water bodies and water quality indicators in them. Predictors of the probability of oxygen stress are the proximity of the sandy border to the reservoir and the elongated shape of the reservoir.

Ecotoxicological consequences of polystyrene naturally leached in pure, fresh, and saltwater: lethal and nonlethal toxicological responses in Daphnia magna and Artemia salina

Polystyrene is widely used in disposable products and is now a ubiquitous plastic pollutant in aquatic environments, where it degrades into smaller particles that leach potentially toxic chemicals. However, knowledge regarding the impacts of plastic leachates remains limited. This study investigates the lethal and nonlethal effects of polystyrene leachate on two ecologically significant aquatic organisms, Daphnia magna (water flea) and Artemia salina (brine shrimp). Polystyrene leachates were prepared in seawater, freshwater, and sterile, pure water by incubating the material in each of the media under natural conditions for six months. D. magna and A. salina were exposed to varying concentrations of the leachates under controlled laboratory conditions, monitoring their survival, as well as measuring reactive oxygen species and antioxidant responses as superoxide dismutase and catalase activity. The data show that A. salina was more significantly affected with higher mortality observed at lower leachate concentrations, potentially linked to seawater enhancing the leaching of toxic additives. Moreover, at non-lethal concentrations, the antioxidative responses maintained homeostasis in both organisms. Considering the current reported microplastic concentrations in the aquatics and the adequate antioxidative response, leachate from plastic potentially does not pose a severe threat to these organisms. Nevertheless, hydrological characteristics of waterbodies may cause microplastic hotspots, which could significantly concentrate plastics and thus their leachates, necessitating action to reduce the current microplastic pollution level and avoid future surges. This study highlights the ecological significance of polystyrene pollution, emphasizing the need for more comprehensive regulatory measures and the development of sustainable alternatives to polystyrene-based products. The distinct responses of D. magna and A. salina imply that the impact of plastic pollution varies among species, necessitating further research to elucidate broader ecological consequences. Understanding how polystyrene leachate affects keystone species provides crucial insights into the overall implications for aquatic ecosystems.

Ecological monitoring of aquatic and terrestrial ecosystems based on the distribution of model groups of living organisms

The paper presents the results of the analysis of the use of living organisms as bioindicators and model groups in the ecological monitoring of natural ecosystems in various landscapes. The results of the analysis of the distribution of dragonflies and macromycetes in aquatic and terrestrial ecosystems, the taxonomic composition of populations and individual communities showed a relationship with physical-geographical and natural-climatic conditions. The distribution of dragonflies is heterogeneous, associated with the characteristic features of water bodies and terrestrial biotopes, the nature of saprobity and plant formations. The distribution of macromycetes is determined by substrate specialization, the composition of the dendroflora of mycobiotes of different altitudinal zones and belts. Features of the distribution, allows you to use these groups of organisms as model objects for the organization of environmental monitoring.