Search Results (161)

This study investigates the distribution patterns, interspecific relationships, and community stability mechanisms of phytoplankton functional groups, aiming to elucidate the ecological processes that drive phytoplankton communities in aquatic ecosystems of arid regions. We conducted seasonal sampling from 2023 to 2024 at four auxiliary reservoirs in the Tarim River Basin, namely Shangyou Reservoir (SY), Shengli Reservoir (SL), Duolang Reservoir (DL), and Xinjingzi Reservoir (XJZ). In recent years, researchers have grouped phytoplankton into functional groups based on their shared morphological, physiological, and ecological characteristics—with these three types of traits serving as the core criteria for distinguishing different functional groups. A total of 18 functional groups were identified from the phytoplankton collected across four seasons, among which eight (A, D, H1, L0, M, MP, P, and S1) are dominant. Redundancy Analysis (RDA) indicated that environmental factors such as pH, electrical conductivity (COND), and dissolved oxygen (DO) are key driving factors affecting phytoplankton functional groups. Interspecific association analysis showed that the phytoplankton communities in DL, SL, and XJZ reservoirs were dominated by positive associations, reflecting stable community structures that are less prone to drastic fluctuations under stable environmental conditions. In contrast, the SY Reservoir was dominated by negative associations, indicating that it is in the early stage of succession with an unstable community. This may be related to intense human disturbance to the reservoir and its role in replenishing the Tarim River, which leads to significant water level fluctuations. The results of the Chi-square test and Pearson correlation analysis showed consistent trends but also differences: constrained by the requirement for continuous normal distribution, Pearson correlation analysis identified more pairs of negative associations, reflecting its limitations in analysing clumped-distributed species. Random forest models further indicated that functional groups M, MP, L0, and S1 are the main positive drivers of interspecific relationships. Among them, the increase in S1 can promote the growth of functional groups dominated by Navicula sp. and Chroococcus sp. by reducing resource competition. Conversely, the expansion of functional group H1 inhibits other groups, which is related to its adaptive strategy of resisting photo-oxidation in eutrophic environments. This study reveals the patterns of interspecific interactions and stability mechanisms of phytoplankton functional groups in arid-region reservoirs, providing a scientific basis for the management and conservation of aquatic ecosystems in similar extreme environments. Full article

Microplastic pollution in marine environments represents a significant ecological threat due to its persistence and harmful effects on biodiversity and human health. In Colombia, coastal ecosystems (particularly in La Guajira) have exhibited increasing microplastic concentrations, but systematic monitoring remains limited. This study explored the application of remote sensing, including multispectral satellite imagery (Sentinel-2) and machine learning algorithms, to detect and monitor microplastics in the coastal zone of Riohacha, La Guajira. To inform the model selection and ensure methodological relevance, a focused systematic literature review was conducted, serving as a foundational step in identifying effective remote sensing strategies and machine learning algorithms previously applied to microplastic detection in aquatic environments. Moreover, microplastic samples were collected from four coastal sites on Riohacha’s coast and analyzed via Fourier transform infrared spectroscopy (FTIR), while environmental parameters were recorded in situ. The remote sensing data were processed and integrated with field observations to train linear regression, random forest, and artificial neural network (ANN) models. The ANN model achieved the highest accuracy (MAE = 0.040; RMSE = 0.071), outperforming the other models in estimating the microplastic concentrations. Based on these results, environmental risk maps were generated, identifying critical zones of pollution. The findings support the integration of remote sensing tools and field data for scalable, cost-efficient microplastic monitoring, offering a methodological framework for marine pollution assessment in Colombia and other developing coastal regions. Full article

Functioning as a critical ecotone between terrestrial and aquatic ecosystems, riparian zones exhibit soil enzyme activities that serve as key biomarkers of their nutrient cycling processes. However, despite considerable focus on riparian soil properties, the dynamics and underlying drivers of these enzymatic activities are not yet fully characterized. To this end, soils were systematically sampled across varying widths and depths from three representative riparian zones to quantify the driving forces of physicochemical properties on enzyme activity dynamics. The results showed that the soil enzyme activity was highest in the forest riparian zone and lowest in the farmland riparian zone, with average enzyme activities of 37.95 (μmol·g⁻¹·h⁻¹) and 26.85 (μmol·g⁻¹·h⁻¹), respectively. The width of the riparian zone changes the spatial distribution of soil enzyme activity. The soil enzyme activity is higher in the land edge area far from the river (profile-1) and lower in the water edge area near the river (profile-4), with average enzyme activities of 47.4384 (μmol·g⁻¹·h⁻¹) and 17.0017 (μmol·g⁻¹·h⁻¹), respectively. Moreover, soil water content (SWC) has a strong impact on enzyme activity changes. The increase in soil depth reduces soil enzyme activity, with enzyme activity in the 0–20 cm soil layer being 1.5 times higher than in the 20–50 cm soil layer. Meanwhile, the primary factors influencing changes in soil enzyme activity have gradually shifted from total nitrogen (TN), nitrate nitrogen (NO₃-N), and soil organic carbon (SOC) to the sole control of SOC. Research has shown that human influence strongly interferes with soil enzyme activity in riparian zones. The width of the riparian zone and soil depth serve as key drivers of the spatial distribution of soil enzyme activity by modulating soil environmental factors. The patterns revealed in this study indicate that maintaining appropriate riparian zone width and reducing anthropogenic disturbances can enhance nutrient cycling dynamics at the micro-scale by increasing soil enzyme activity. This process is crucial for strengthening the riparian zone’s macro-level ecosystem services, particularly by effectively enhancing its capacity to sequester and transform nutrients like nitrogen and phosphorus from agricultural nonpoint sources, thereby safeguarding downstream water quality. Consequently, soil enzyme activity serves as a key indicator, providing essential scientific basis for assessing riparian health and guiding ecological restoration efforts. Full article

Artificial water replenishment has improved the ecological environment of West Lake by introducing external clean water, but pollution issues still persist in some local regions. However, whether enhancing water exchange through internal water diversion within the lake can improve local water quality remains unverified. This study employed the Delft3D hydrodynamic model to simulate the spatiotemporal distribution of local water renewal time in West Lake, revealing that regions with prolonged water renewal times exhibited diminished resilience to water quality disturbances. This study utilized the Random Forest algorithm to determine the responsiveness of West Lake’s water transparency to parameters such as local water renewal time, and further discussed the impact of reducing local water renewal time on transparency under different water quality conditions. The results indicate that the sensitivity of West Lake’s transparency to water quality parameters closely resembles that of lakes with rainwater storage. The primary mechanism by which external water diversion improves transparency is through pollutant dilution, whereas enhanced local water exchange capacity contributes minimally to this effect. This conclusion demonstrates that localized internal water diversion within the lake is only suitable for preventing ecological issues such as regional eutrophication and algal blooms, but cannot effectively improve the overall lake ecosystem. Furthermore, this study identifies key factors affecting water transparency in artificially managed waters, highlighting priority monitoring indicators for similar water bodies. It also provides evidence to support research on aquatic optics and the development of remote sensing algorithms for such waters. Full article

Petroleum hydrocarbon pollution is a serious environmental and human health problem. In recent decades, the impact of this substance has been profound and persistent, affecting the balance of aquatic and terrestrial ecosystems and leading to significant physical and psychosocial effects among the population. Natural sources (crude oil, natural gas, forest fires, and volcanic eruptions) and anthropogenic (road traffic, smoking, pesticide use, oil drilling, underground water leaks, improper oil spills, industrial and mining waste water washing, etc.), the molar weight of the hydrocarbon, and the physicochemical properties are important factors in determining the degree of pollution. The effects of pollution on the environment consist of altering the fundamental structures for sustaining life (infertile lands, climate change, and loss of biodiversity). In terms of human health, diseases of the following systems occur: respiratory (asthma, bronchitis), cardiovascular (stroke, heart attack), pulmonary (infections, cancer), and premature death. To reduce contamination, sustainable intervention must be carried out in the early stages of the pollution-control process. These include physical techniques (isolation, soil vapor extraction, solvent extraction, soil washing), chemical techniques (dispersants–surfactants, chemical oxidation, solidification/stabilization, thermal desorption), biological techniques (bioremediation, phytoremediation), and indigenous absorbents (peat, straw, wood sawdust, natural zeolites, clays, hemp fibers, granular slag, Adabline II OS). Due to the significant environmental consequences, decisions regarding the treatment of contaminated sites should be made by environmental experts, who must consider factors such as treatment costs, environmental protection regulations, resource recovery, and social implications. Public awareness is also crucial, as citizens need to understand the severity of the issue. They must address the sources of pollution to develop sustainable solutions for ecosystem decontamination. By protecting the environment, we are also safeguarding human nature. Full article

Mediterranean subtidal reefs host ecologically significant habitats, including forests of Cystoseira spp., which form complex benthic communities within the photic zone. These habitats are increasingly degraded due to climate change, invasive species, and anthropogenic pressures, particularly in the eastern Mediterranean. In support of habitat monitoring under the EU Natura 2000 directive and the Nature Restoration Regulation, this study investigates the utility of high-resolution satellite remote sensing for mapping subtidal brown algae and associated benthic classes. Using imagery from the SuperDove sensor (Planet Labs, San Francisco, CA, USA), we developed an integrated mapping workflow at the Natura 2000 site GR2420009. Aquatic reflectance was derived using ACOLITE v.20250114.0, and both supervised classification and spectral unmixing were implemented in the EnMAP Toolbox v.3.16.3 within QGIS. A Random Forest classifier (100 fully grown trees) achieved high thematic accuracy across all habitat types (F1 scores: 0.87–1.00), with perfect classification of shallow soft bottoms and strong performance for Cystoseira s.l. (F1 = 0.94) and Seagrass (F1 = 0.93). Spectral unmixing further enabled quantitative estimation of fractional cover, with high predictive accuracy for deep soft bottoms (R² = 0.99; RPD = 18.66), shallow soft bottoms (R² = 0.98; RPD = 8.72), Seagrass (R² = 0.88; RPD = 3.01) and Cystoseira s.l. (R² = 0.82; RPD = 2.37). The lower performance for rocky reefs with other cover (R² = 0.71) reflects spectral heterogeneity and shadowing effects. The results highlight the effectiveness of combining classification and unmixing approaches for benthic habitat mapping using CubeSat constellations, offering scalable tools for large-area monitoring and ecosystem assessment. Despite challenges in field data acquisition, the presented framework provides a robust foundation for remote sensing-based conservation planning in optically shallow marine environments. Full article

The clarification of community assembly mechanisms in benthic macroinvertebrates and their respective contributions to the development of beta diversity is a fundamental concern in aquatic ecology. Nonetheless, the intrinsic complexity of community alterations and their non-linear reactions to gradients of explanatory variables present considerable obstacles to measuring the determinants of beta diversity. Fifty sampling points were set up along the major rivers of the Yellow River Irrigation Area (YRIA), the Central Arid Zone (CAZ), and the Southern Mountainous Area (SMA) in Ningxia in April, July, and October 2023. The findings demonstrate that the optimal parameter-based geographical detector (OPGD) model identified a 3000 m circular buffer as the spatial scale at which landscape structure most significantly influences water quality. A degradation in water quality presumably results in diminished differences in species composition among communities. The Sørensen index was determined to be more appropriate for this investigation, and the total beta diversity of the communities was relatively high (β_SOR ≥ 0.82), with no identifiable nested spatial patterns detected. Except in the YRIA, environmental variability contributed more significantly to the variance in beta diversity than spatial factors, and deterministic mechanisms dominated the community assembly of benthic macroinvertebrates across all three months. To improve biodiversity and aquatic ecosystem health, the study region should optimize its landscape structure by reducing the amount of bare land and increasing the percentage of forest land within buffer zones. Additionally, a multi-site conservation strategy should be put into place. Full article

Riparian forests are important for maintaining aquatic biodiversity, yet they face increasing pressure from logging activities. This study assessed the functional diversity of Ephemeroptera, Plecoptera, and Trichoptera (EPT) in 30 Amazonian first-order streams across three riparian forests: pristine, selectively logged, and conventionally logged. We evaluated four habitat attributes linked to ecosystem functioning (canopy cover, water temperature, sediment organic matter, and small woody debris) and calculated two indices of functional diversity: richness and divergence. Functional diversity was highest in pristine streams, intermediate in selectively logged streams, and lowest in conventionally logged streams. Functional richness and divergence declined significantly in conventionally logged forests, indicating a loss of ecological traits and potential reductions in ecosystem functions. We also observed that canopy cover, sediment organic matter, and woody debris were positively associated with EPT functional diversity, while water temperature had a negative association. These findings highlight that conventional logging leads to the functional homogenization of aquatic insect assemblages, compromising key ecological processes. Selective logging that maintains riparian buffers may preserve functional diversity, even though these differences may be influenced by site-specific environmental conditions. Our results underscore the importance of conserving riparian integrity to sustain the resilience and functioning of tropical stream ecosystems in logged landscapes. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

Headwater streams serve as a crucial link between forest and downstream aquatic ecosystems and also act as crucial agents in carbon (C) and nutrient storage and flux. These aquatic systems play a pivotal role in regulating biogeochemical cycles. Plant litter is an important contributor of nutrients to headwater streams, having significant impacts on downstream ecosystems. However, current research predominantly focuses on the dynamics of plant litter C and nutrients such as nitrogen and phosphorus, and we know little about those of nutrients such as sodium (Na). In this study, we conducted a comprehensive evaluation of the annual dynamics of plant litter Na storage within a subtropical headwater stream. This study took place over a period of one year, from March 2021 to February 2022. Our results showed that (1) the average annual concentration and storage of litter Na was 538.6 mg/kg and 2957.6 mg/m², respectively, and litter Na storage exhibited a declining trend from stream source to mouth, while demonstrating significantly higher values during the rainy season compared to the dry season; (2) plant litter type had significant impacts on Na concentration and storage, with leaf, twig, and fine woody debris accounting for the majority of litter Na storage; and (3) hydrological (precipitation, discharge) and physicochemical (water temperature, flow velocity, pH, dissolved oxygen, alkalinity) factors jointly affected Na storage patterns. Overall, the results of this study clearly reveal the dynamic characteristics of Na storage in plant litter in a subtropical forest headwater stream, which contributes to a more comprehensive understanding of the role of headwater streams in nutrient cycling and the dynamic changes of nutrients along with hydrological processes. This research will enhance our predictive understanding of nutrient cycling at the watershed scale. Full article

Riparian forests are usually situated between terrestrial and aquatic systems. They play an essential role in the health of the environment and in providing complex ecosystem services. This is especially essential in arid and semi-arid regions. However, despite these facts, riparian ecosystems are underexplored in the specialty literature. As such, the purpose of this study is to address this gap by synthesizing the current knowledge about riparian forests, using both a bibliometric analysis and a qualitative literature approach. This analysis allowed us to identify six main ecosystem services provided by riparian forests: biodiversity support, carbon sequestration, water quality regulation, slope stability, pollution mitigation, and sociocultural benefits. Furthermore, we have emphasized local challenges (deforestation, agricultural expansion, a lack of policies). Connecting ecological knowledge with a socio-cultural context is the first step in creating a strong foundation for the adequate management of these essential ecosystems, while also supporting their conservation, development and climate resilience. Full article

Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution by integrating automated monitoring systems (AMSs) with advanced machine learning (ML) techniques to predict chlorophyll-a ($Chl-a$) concentrations. Utilizing low-cost and readily available input variables, we developed energy-efficient ML algorithms optimized for deployment on buoys with a battery and hardware resources. The AMS employs preprocessing methods like the SMOTE and Random Forest (RF) for feature selection and ranking. Deep feature extraction is performed through a ResNet-18 model, while temporal dependencies are captured using a Long Short-Term Memory (LSTM) network. A Softmax output layer then predicts $Chl-a$ concentrations. An alert system is incorporated to warn when $Chl-a$ levels exceed 10 μg/L, signaling potential bloom conditions. The results show that this approach offers a rapid, cost-effective, and scalable solution for real-time water quality monitoring, enhancing manual sampling efforts and improving management of water bodies at risk. Full article

Lake monitoring is essential for sustaining aquatic ecosystems, and accurate estimation/prediction of water quality parameters is crucial to this effort. Despite its importance, the performance of predictive models built on varying temporal resolutions remains underexplored systematically. This study used daily and 4 h high temporal resolution (HTR) datasets to assess the performance of multiple machine learning models—namely, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks—under consistent data scales. The results indicate that dissolved oxygen (DO) exhibits pronounced sensitivity to temporal resolution, while total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₃-N) show distinct, parameter-specific response patterns that align with the temporal characteristics of their underlying biogeochemical processes. This research helps to deepen the understanding of how temporal data resolution influences model performance in water quality prediction, offering valuable insights for selecting optimal data resolutions and modeling techniques to enhance lake monitoring and protection strategies. Full article

This study investigated the impact of land use change on water quality in the Xinxian River Basin amidst rapid urbanization. While previous studies have predominantly focused on single-scale buffer analyses or specific land use types, the interactions between multi-scale riparian buffers and diverse land cover dynamics remain rarely understudied, particularly in a rapidly urbanizing county in the Yangtze River Basin. Land use type data for the Xinxian River Basin in 2000, 2010, and 2020 were acquired using GIS technology, and subsequent analysis quantified land use pattern changes over this 20-year period. Additionally, 2023 land use data for riparian buffer zones (50 m, 100 m, 200 m, 400 m, and 600 m) were obtained via GIS and subjected to Redundancy Analysis (RDA) with 2023 water quality monitoring data to evaluate the impact of land use on water quality. The results revealed significant land use conversion dynamics, particularly between natural and anthropogenic cover types. Forest cover exhibited negative correlations with riverine nutrient concentrations, while built-up areas displayed strong positive associations, especially at finer scales (50–100 m buffers). Notably, the dominant influencing factor shifted from built-up land at smaller buffer scales (50–100 m) to forest land at larger scales (400–600 m), whereas agricultural land showed no significant correlation. These findings highlight scale-dependent relationships between land use and aquatic ecosystems, emphasizing the critical role of spatial planning in mitigating urbanization impacts. The work is conducive to the sustainable development of Longgan Lake National Wetland Nature Reserve and the protection of water ecology in the middle and lower reaches of the Yangtze River. Full article

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep forest (DF), gradient boosting (GB), and XGBoost, and compared their performance against individual models, such as support vector machine (SVM), decision tree (DT), and multi-layer perceptron (MLP). The findings revealed that the ensemble models, particularly XGBoost and deep forest (DF), achieved superior predictive accuracy, with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAPs) provided insights into the relative importance of the input features, identifying the particulate organic nitrogen (PON), particulate organic carbon (POC), and total phosphorus (TP) as the critical factors influencing the Chl-a concentrations. This research demonstrates the effectiveness of ensemble ML models for achieving high predictive accuracy, while the integration of XAI enhances model interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring. Full article