Search Results (719)

Chromium (Cr) is a widespread heavy metal contaminant in aquatic environments, posing serious risks to phytoplankton due to its persistence, biotoxicity, and mutagenic potential. Microalgae have emerged as promising biological agents for Cr remediation. In this study, the Cr removal potential of living Dunaliella salina (D. salina) was evaluated by examining the toxic effects and adsorption behavior of trivalent Cr(III) and hexavalent Cr(VI) through short-term exposure experiments. This study elucidated the mechanisms by which Cr disrupts key photosynthetic metabolic pathways, quantified the short-term toxicity thresholds of Cr(III) and Cr(VI) to D. salina, and characterized the saturation adsorption capacity and adsorption kinetics of Cr on algal cells. The results showed that Cr(VI) at concentrations of 5–20 mg/L inhibited the growth of D. salina in a dose-dependent manner throughout the culture period, with inhibition rates ranging from 22.8% to 70.9%. After 72 h of exposure, the maximum growth inhibition rates caused by Cr(III) and Cr(VI) reached 42.5% and 52%, respectively. Interestingly, low concentrations of Cr(VI) (0.1–1 mg/L) slightly enhanced the growth of D. salina. However, Cr(VI) exhibited stronger biotoxicity than Cr(III). Exposure to both Cr species significantly reduced the levels of chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car), resulting in damage to the photosynthetic reaction centers and suppression of the photosynthetic electron transport system. The adsorption of Cr(VI) by D. salina followed a pseudo-second-order kinetic model, with a maximum adsorption capacity of 38.09 mg/g. The process was primarily governed by monolayer chemisorption. These findings elucidate the toxic mechanisms of Cr in D. salina and highlight its potential application as an effective bioremediation agent for heavy metal pollution, particularly Cr(VI), in marine environments. Full article

Traditional water quality monitoring methods are limited in providing timely chlorophyll-a (Chl-a) assessments in small inland reservoirs. This study presents a rapid Chl-a retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-a maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs. Full article

Chlorophyll-a (Chl-a) concentration is a key indicator of coastal ecosystem health, reflecting both primary productivity and the ecosystem’s response to climate change and human activities. This study quantifies long-term Chl-a trends in the Yellow and Bohai Seas using a multi-source remote sensing reconstruction dataset generated with deep learning algorithms. Quantile regression was applied to assess changes across the 75th, 50th, and 25th percentiles, and environmental drivers—including sea surface temperature, mixed layer depth, wind speed, and sea surface height anomalies—were evaluated in representative regions such as estuaries, aquaculture zones, and offshore waters. From 2005 to 2024, Chl-a concentrations declined across the 75th, 50th, and 25th percentiles, with rates of −4.82 × 10⁻³, −4.50 × 10⁻³, and −4.09 × 10⁻³ mg·m⁻³·a⁻¹, respectively (where “a” denotes year). The decline also showed strong seasonal differences, with summer decreases (−0.0638 mg·m⁻³·a⁻¹) substantially greater than winter (−0.04 mg·m⁻³·a⁻¹). Spatially, the decline was more pronounced in high-concentration nearshore waters, with rates of −0.0283 mg·m⁻³·a⁻¹ in the Qinhuangdao region, compared to −0.0137 mg·m⁻³·a⁻¹ in deeper offshore waters. Mixed-layer depth and wind speed emerged as the primary physical controls, with nearshore declines driven by enhanced vertical mixing and offshore changes dominated by mesoscale oceanic processes. These findings provide new insights for modeling and managing coastal ecosystems under combined climate and anthropogenic pressures. Full article

Polyploidization is a powerful tool in plant breeding that can induce desirable morphological and physiological modifications. This study aimed to establish an efficient in vitro protocol for inducing autotetraploid plants in cherimoya (Annona cherimola Mill. cv. Fino de Jete) using colchicine. Hypocotyl explants from seedlings germinated in vitro were treated with different colchicine concentrations (0.01–0.2%) for 24 and 48 h, and the effects on shoot regeneration and ploidy level were evaluated by flow cytometry and chromosome counting. Regeneration and survival rates decreased with increasing colchicine concentration and exposure time. The most effective treatment for autotetraploid induction was 0.1% colchicine for 24 h, yielding a 10.5% polyploidization rate with 5.8% autotetraploids. Tetraploid shoots were successfully rooted (80%) and acclimatized (100%) under greenhouse conditions. Autotetraploid plants exhibited significantly larger and more rounded leaves, higher chlorophyll contents and an increased Chl a/Chl b ratio compared with diploids, indicating enhanced photosynthetic efficiency. The induction of stable autotetraploid lines in A. cherimola provides a reliable approach for generating novel genotypes with improved physiological traits and potential tolerance to abiotic stress. These results offer valuable material for future breeding programs aimed at developing new cherimoya rootstocks and cultivars with enhanced vigor and adaptability. Full article

Chlorophyll-a (Chl-a) concentration serves as a crucial indicator for assessing phytoplankton biomass and marine ecological health. This study investigated the spatiotemporal characteristics and influencing factors of Chl-a in Shenzhen’s coastal waters using high-frequency monitoring data from 13 buoys deployed from January 2023 to January 2024. The research methodology incorporated comprehensive statistical analyses, including correlation analysis to identify relationships between Chl-a and environmental parameters and a linear mixed model, as well as stepwise regression analysis to determine the dominant factors controlling Chl-a variability across different sea areas. Results revealed distinct spatiotemporal patterns: seasonal Chl-a concentrations ranked as summer > autumn > winter > spring. Spatially, western waters (Pearl River Estuary and Shenzhen Bay) exhibited elevated levels from winter to summer, whereas the eastern Daya Bay peaked in autumn. Mechanistically, regional drivers diverged significantly. River runoff dominated Chl-a variability in the Pearl River Estuary. Temperature and runoff co-regulated dynamics in Shenzhen Bay. Wind-driven mixing and nutrients were the primary controls in Daya Bay, while oligotrophic conditions maintained low levels in Mirs Bay. Salinity and temperature were universal regulators, but nutrient limitations were region-specific, with phosphorus limitation in Shenzhen Bay and nitrogen limitation in Mirs Bay. The high-frequency buoy data effectively captured complex spatiotemporal variability, providing valuable insights for developing targeted management strategies to mitigate red tide risks and improve water quality in these coastal ecosystems. Full article

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions. Full article

To investigate the influence of land use landscape patterns on lake water quality in the basin, the land use and water quality data of the Yilong Lake Basin from 1993 to 2023 were analyzed with a geographic information system, remote sensing, and landscape ecology methods in this research. The results show that (1) the land use landscape pattern and water quality of the Yilong Lake Basin had significant changes: the lake surface area, farmland, and shrubland declined, with grassland showing the sharpest decrease and serving as the main source of conversion to other land types, while forest land expanded and built-up land increased by five times. The landscape pattern analysis showed that the aggregation degree of the core habitat in the basin increased and the landscape had decreased patch density and increased heterogeneity. Regarding water quality, the concentrations of total nitrogen (TN), total phosphorus (TP), and ammonium nitrogen (NH₄⁺-N); permanganate index (I_Mn); and biochemical oxygen demand over 5 days (BOD₅) decreased. Furthermore, the concentration of dissolved oxygen (DO) increased and the concentration of chlorophyll-a (Chl-a) fluctuated for a long time but did not decrease dramatically at the end of the period compared with the beginning. In general, the eutrophication degree of Yilong Lake slightly decreased. (2) The landscape configuration strongly shaped the water quality: the redundancy analysis (RDA) revealed that the edge density (ED), landscape shape index (LSI), largest patch index (LPI), and patch density (PD) were negatively associated with the eutrophication of Yilong Lake (TN, TP, NH₄⁺-N, Chl-a), whereas the contagion index (CONTAG) was positively associated; the Shannon’s diversity index (SHDI) was closely linked with TN and I_Mn but negatively with DO; and the patch cohesion index (COHESION) had a low interpretation power for water quality changes. In particular, larger and more cohesive ecological patches supported a higher DO, while an increased patch density was linked to an elevated I_Mn and reduced DO. These results indicate that the restoration of key ecological patches and enhanced landscape cohesion helped to improve the water quality, whereas increased patch density and landscape heterogeneity negatively affected it. (3) In the past 30 years, the ecological management and protection work on Yilong Lake, such as returning farmland to forests and lakes, wetland restoration, and sewage pipe network construction, achieved remarkable results that were reflected in the change in the relationship between land use landscape pattern and water quality in the basin. However, human activities still affected the dynamic evolution of water quality: the expansion of built-up land increased the patch density, the reduction in shrubland and grassland weakened natural filtration, and the rapid urbanization process introduced more pollution sources. Although the increase in forest land helped to improve the water quality, the effect was not fully developed. These findings provide a scientific basis for the management and ecological restoration of plateau lakes. Strengthening land use planning, controlling urban expansion, and maintaining ecological patches are essential for sustaining water quality and promoting the coordinated development of the ecology and economy in the Yilong Lake Basin. Full article

Background/Objectives: Stenotrophomonas maltophilia (SM) is a significant cause of hospital-acquired infections in immunocompromised and critical care patients. This study investigates the impact of combining ceftazidime/avibactam (CZA) with conventional antibiotics on SM obtained from various sources in planktonic and biofilm cell cultures. Methods: Using broth microdilution, the MICs of different antibiotics, including CZA, were determined on 37 SM strains. CZA’s bactericidal and synergistic effectiveness were examined through in vitro time–kill curve tests with tigecycline (TGC), chloramphenicol (CHL), levofloxacin (LVX), colistin (CS), and amikacin (AMK). In addition, synergistic activity was investigated against SM biofilm cell cultures, and antibiotic Mutant Prevention Concentrations (MPCs) were tested against SM isolates. Results: Compared to ceftazidime (CAZ), CZA was four times more efficient against 37 SM strains. Unlike TGC and CHL, CS, AMK, and CZA had 2–4 times higher MBCs than MICs. All studied antibiotics were bactericidal at 1× or 4× MIC doses against SM bacteria, except for CZA. The combinations of CZA with LVX and CZA with AMK or CS demonstrated synergistic effects in four out of seven (57%) strains and in three out of seven (43%) strains, respectively, when tested at doses equivalent to the MIC. Moreover, all antibiotic combinations with CZA showed a synergistic effect at dosages four times the MIC. Additionally, CZA and the tested drugs synergistically inhibited SM biofilm formation, and MPC values were 8–16 times the MIC. Conclusions: The results of this study indicate that combining CZA with LVX and CS was more effective against SM strains. These combinations might provide alternatives for treating SM pathogens in both planktonic and biofilm cell cultures. Full article

Submesoscale dynamics are critical modulators of upper-ocean biogeochemistry, yet their net influence on chlorophyll concentrations across seasonal to interannual timescales, particularly within productive regions like the Benguela Current Large Marine Ecosystem (BCLME), remains poorly understood. This study quantifies these complex relationships by analyzing 22 years (2001–2022) of physical and biological data. We examined the link between surface chlorophyll (CHL) and key physical drivers: sea level anomaly (SLA) and submesoscale intensity, quantified by the Rossby number (Ro). Using both cross-correlation analysis and Generalized Linear Models (GLMs), our analyses reveal a multi-scale set of spatially dependent and time-lagged biogeochemical responses. At the basin scale, a key finding from cross-correlation is a significant positive correlation where high SLA precedes a rise in CHL by approximately six months, indicating a delayed ecosystem response to large-scale physical forcing. At the event scale, GLMs show the specific impact of eddies is critical: short-lived cyclonic eddies correlate with a significant increase in CHL (~4.6%) in the southern zone, while anticyclonic eddies are associated with a pronounced decrease in CHL (~97.7%) in the central zone during the austral winter. These findings demonstrate that both large-scale preconditions and localized submesoscale features are essential drivers of vertical nutrient transport and the distribution of primary productivity within the BCLME. Full article

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article

Coastal lagoons are recent geological formations, crucial biodiversity hot-spots, and fragile ecosystems which provide several ecosystem services. These areas are strongly affected by nutrient inputs, which can lead to eutrophication and algal blooms. We identified nine Italian coastal lagoons with a surface area greater than 10 km². Most of them were previously classified in a poor ecological condition. Therefore, we used remote sensing, in particular Sentinel-2 images, to assess the trophic state of these areas over time from 2015 until 2025. Automatic products of chlorophyll-a (Chl-a), total suspended matter (TSM), and water transparency (kd_z90max) were derived. Chl-a concentrations indicated predominantly eutrophic conditions, ranging from 0.44 (Mare Piccolo) to 80.81 mg·m⁻³ (Comacchio). Comacchio and Cabras showed persistently high Chl-a values and low transparency, while Mare Piccolo was characterized by high transparency and oligotrophic conditions. Varano and Cabras showed a significant increase in Chl-a (p < 0.05) coupled with an increase in TSM (p < 0.01) and decline in transparency in Varano (p < 0.05). Most other lagoons showed no long-term trends but remained in eutrophic–hypereutrophic states. Therefore, the Italian coastal lagoons studied are vulnerable areas to environmental degradation. Many of the lagoons showed persistent eutrophic conditions and no long-term recovery trends. However, among the lagoons, there were heterogeneous ecological conditions, ranging from oligotrophic (Mare Piccolo) to chronically hypereutrophic (Comacchio, Cabras). Water clarity was mainly affected by suspended solids; however, in some cases, there was a key role in primary production (algal blooms). Sentinel-2 data proved effective for monitoring spatial and temporal variability in coastal lagoon water quality, offering a valuable tool for environmental management and early detection of degradation trends. Full article

Lake restoration, achieved through a combination of biomanipulation and the recovery of submerged macrophytes, can effectively reduce nutrient concentrations, thereby suppressing phytoplankton biomass. Nevertheless, there is limited knowledge regarding the impact of nutrient limitation in phytoplankton biomass on lake restoration efforts. We compared the changes in nutrient levels and phytoplankton biomass (measured by chlorophyll a, Chl a) between restored and unrestored areas of a subtropical shallow Lake Yiai. Furthermore, we assessed the nutrient limitation patterns in these two areas through field nutrient addition experiments conducted during the summer. Monitoring results indicated that mean concentrations of Chl a and nutrients were significantly lower (t-test p < 0.0001) in the restored area compared to the unrestored area. In the nutrient addition experiment, phytoplankton biomass was nitrogen-limited in the unrestored part, whereas it was co-limited by both nitrogen and phosphorus in the restored area. These findings suggest that nutrient limitation may serve as a crucial mechanism in sustaining low phytoplankton biomass following the restoration of shallow lakes, particularly during the summer season, with the recovery of submerged macrophytes. Full article

Populus qamdoensis cuttings were treated with transparent colorless film (WF), blue film (BF), and green film (GF), and the leaf physiological indices were measured and transcriptome sequencing was performed. The results showed that BF treatment significantly inhibited the height growth, leaf length, and leaf width of P. qamdoensis, while significantly increasing the thickness of leaf palisade tissue, upper epidermis, and the density of leaf structure. The GF treatment increased stomatal conductance (Gs) and intercellular CO₂ concentration (Ci), while the BF treatment enhanced water use efficiency (WUE). Both BF and GF increased the contents of chlorophyll b (Chl b) and carotenoids (Car). BF treatment increased the content of total soluble sugars but decreased the contents of sucrose and starch. Transcriptome analysis revealed that under BF treatment, most genes in the sucrose and starch metabolism pathways were up-regulated, and the AUX/IAA, GH3, and SAUR genes in the auxin pathway also showed an up-regulated trend. In contrast, under GF treatment, most genes in the porphyrin and chlorophyll metabolic pathway were up-regulated, and most genes in the gibberellin pathway also showed up-regulation. Analysis of photoreceptor gene expression showed that GF treatment significantly up-regulated the expression of HYH, COP1, CRY1, HY5, and PIF4 genes, while BF treatment had the opposite effect. These results provide a theoretical basis for revealing the evolutionary mechanisms underlying the adaptation of plants to different light environments. Full article

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales. Full article

Understanding the alignment between fisher behavior and habitat dynamics is essential for data-driven fisheries management. This study analyzed high-resolution Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data, integrated with logbooks from 10 stick-held dipnet vessels targeting Pacific saury (Cololabis saira) in the North Pacific high seas. We developed an optimized CNN-LSTM-SE model to classify vessel trajectories into eight operational states, achieving 91% accuracy. This model generated a high-confidence presence dataset, addressing spatiotemporal data limitations in pelagic species research. A dynamic Ensemble Species Distribution Model (ESDM) mapped habitat suitability index (HSI) for the primary fishing seasons (June–September) of 2023–2024, revealing seasonal northward migrations and an interannual eastward shift in core habitats, primarily driven by sea surface temperature (SST: 6.4–19.1 °C), chlorophyll-a (CHL: 0.2–2.0 mg/m³), mixed layer depth (MLD: 14–30 m), and dissolved oxygen (DO: 220–290 mmol/m³). Receiver operating characteristic (ROC) sensitivity analysis identified an HSI threshold of ≥0.4 for suitable habitats, where 98.4% of fishing effort was concentrated. Kaplan–Meier survival analysis demonstrated that vessels in high-quality habitats (HSI ≥ 0.8) exhibited significantly longer fishing bout durations and lower cessation probabilities (log-rank test, χ² = 20.9, p < 0.001), providing empirical evidence for the Marginal Value Theorem and Optimal Foraging Theory. Although HSI showed a weak direct correlation with catch rates (R² = 0.007), it effectively delineated high-potential fishing grounds (>90% of high-catch days > 30 tonnes in HSI ≥ 0.6). By demonstrating that fishers’ spatial decisions appear to reflect environmental gradients, suggesting that fishing effort may indirectly act as an ecological indicator, this integrated framework bridges fisher behavior with ecological theory, supporting dynamic ocean management in climate-variable fisheries. Full article