Search Results (680)

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

Arctic shelf waters exhibit high optical variability due to terrestrial inputs and elevated colored dissolved organic matter (CDOM) concentrations, posing significant challenges for the accurate retrieval of chlorophyll-a (Chl-a) and downwelling diffuse attenuation coefficients (${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$). These retrieval biases contribute to substantial uncertainties in estimates of primary productivity and upper-ocean heat flux in the Arctic Ocean. However, the performance and constraints of existing ocean color algorithms in Arctic shelf environments remain insufficiently characterized, particularly under seasonally variable and optically complex conditions. In this study, we present a systematic multi-year evaluation of commonly used empirical and semi-analytical ocean color algorithms across the western Arctic shelf, based on seven expeditions and 240 in situ observation stations. Building on these evaluations, regionally optimized retrieval schemes were developed to enhance algorithm performance under Arctic-specific bio-optical conditions. The proposed OCx-AS series for Chl-a and ${{\rm K}}_{\mathrm{d}}$-DAS models for ${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$ significantly reduce retrieval errors, achieving RMSE improvements of over 50% relative to global standard algorithms. Additionally, we introduce QAA-LS, a modified semi-analytical model specifically adapted for the Laptev Sea, which addresses the strong absorption effects of CDOM and corrects the significant overestimation observed in previous QAA versions. Full article

The chlorophyll-a (Chl-a) concentration in lakes is a crucial parameter for monitoring water quality and assessing phytoplankton abundance. However, accurately retrieving Chl-a concentrations remains a significant challenge in remote sensing. To address the limitations of existing methods in terms of modeling efficiency and adaptability, this study focuses on Lake Nanyi in Anhui Province. By integrating Sentinel-2 satellite imagery with in situ water quality measurements and employing the AutoML framework AutoGluon, a Chl-a inversion model based on narrow-band spectral features is developed. Feature selection and model ensembling identify bands B6 (740 nm) and B7 (783 nm) as the optimal combination, which are then applied to multi-temporal imagery from October 2022 to generate spatial mean distributions of Chl-a in Lake Nanyi. The results demonstrate that the AutoGluon framework significantly outperforms traditional methods in both model accuracy (R²: 0.94, RMSE: 1.67 $\mathsf{\mu }$g/L) and development efficiency. The retrieval results reveal spatial heterogeneity in Chl-a concentration, with higher concentrations observed in the southern part of the western lake and the western side of the eastern lake, while the central lake area exhibits relatively lower concentrations, ranging from 3.66 to 21.39 $\mathsf{\mu }$g/L. This study presents an efficient and reliable approach for lake ecological monitoring and underscores the potential of AutoML in water color remote sensing applications. Full article

Research on young trees’ adaptation to shade has predominantly focused on leaf-level responses, overlooking critical structural and functional adaptations in branch systems. In this study, we address this gap by investigating hierarchical branch morphology–physiology integration in 20-year-old Pinus koraiensis specimens across four distinct light conditions classified by photosynthetic photon flux density (PPFD): three in the understory (low light, LL: 0–25 μmol/m²/s; moderate light, ML: 25–50 μmol/m²/s; and high levels of light, HL: 50–100 μmol/m²/s) and one under full light as a control (FL: 1300–1700 μmol/m²/s). We measured branch base diameter, length, and angle as well as chlorophyll and NSCs content in branches and needles. Branch base diameter and length were more than 1.5-fold higher in the FL Korean pine trees compared to the understory-grown ones, while the branching angle and ratio in the LL Korean pine trees were more than two times greater than those in the FL trees. As light levels increased, Chlorophyll a and b and total chlorophyll (Chla, Chlb, and Chl) concentrations in the needles all significantly decreased. Starch, glucose, and NSC (Starch + Soluble Sugars) concentrations in both needles and branches were the highest in the trees under FL and lowest under ML (except for soluble sugars in branches). Understory young P. koraiensis trees morphologically and physiologically adapt to limited light conditions, growing to be more horizontal, synthesizing more chlorophyll in needles, and attempting to increase their light-foraging ability. We recommend gradually expanding growing spaces to increase light availability for 20-year-old Korean pine trees grown under canopy level. Full article

Native and invasive plants of the riverain region undergo a range of environmental stresses that result in excess reactive oxygen species (ROS). Hydrogen peroxide (H₂O₂) is a relatively stable and quickly quantifiable way among different ROS. The herbaceous species including Artemisia princeps, Sicyos angulatus, and Solidago altissima were selected. The H₂O₂ and photosynthetic pigment of leaves were measured, soil samples were analyzed to quantify macronutrients such as total nitrogen (TN), total phosphorus (TP), and soil moisture, and photosynthetic photon flux density (PPFD) was also recorded at different observed sites of Arakawa Tarouemon, Japan. The H₂O₂ concentration of S. altissima significantly increased with high soil moisture content, whereas A. Princeps and S. angulatus significantly decreased with high soil moisture. In each species, H₂O₂ was negatively correlated with chlorophyll a (chl a) and chlorophyll b (chl a). When comparing different parameters involving TN, TP, PPFD, and soil moisture content with H₂O₂ utilizing the general additive model (GAM), only soil moisture content is significantly correlated with H₂O₂. Hence, this study suggests that H₂O₂ would be an effective biomarker for quantifying environmental stress within a short time, which can be applied for riparian native and invasive plant species vegetation regulation. Full article

Lactic acid bacteria (LAB) preserve many foods and play a vital role in fermented food products. This study designed a controlled biotechnological process of catfish (Clarias gariepinus) fermentation with a LAB starter culture isolated from corn hydrolysate. The BY (Barbu-Yelouassi) LAB strain was characterized regarding fermentative and antimicrobial potential, and its adaptability in the simulated gastrointestinal system (SGIS). After 10–12 h of cultivation on MRS broth (De Man Rogosa and Sharpe), the strain achieved the maximum exponential growth, produced maximum lactic acid (33.04%), and decreased the acidity up to pH 4. Also, the isolated strain showed increased tolerance to an acidic pH (3.5–2.0), high concentrations of salt (2–10%), and high concentrations of bile salts (≤2%). The behavior in SGIS demonstrated good viability after 2 h in artificial gastric juice (AGJ) (1 × 10⁷ CFU/mL) and up to 2 × 10³ CFU/mL after another 6 h in artificial intestinal juice (AIJ). The characterized BY strain was identified with the API 50CHL microtest (BioMerieux) as Lactiplantibacillus pentosus (Lbp. pentosus) (90.9% probability), taxon confirmed by genomic DNA sequencing. It was also demonstrated that Lbp. pentosus BY inhibited the growth of pathogenic bacteria, including Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, and sporulated bacteria, such as Bacillus cereus. Additionally, it suppressed the sporulation of fungi like Aspergillus niger, Fusarium sp., and Penicillium sp. Furthermore, the Lbp. pentosus BY strain was used to ferment catfish, resulting in three variants of lanhouin (unsalted, with 10% salt, and with 15% salt), which exhibited good microbiological safety. Full article

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized as ecological indicators that enable a quantitative assessment of the status of marine ecosystems in response to environmental change. Here, using an extensive, updated in situ pigment dataset collated from numerous past research campaigns across the Mediterranean Sea, we re-parameterized an abundance-based phytoplankton size class model that infers Chl-a concentration in three phytoplankton size classes: pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm). Following recent advancements made within this category of size class models, we also incorporated information of sea surface temperature (SST) into the model parameterization. By tying model parameters to SST, the performance of the re-parameterized model was improved based on comparisons with concurrent, independent in situ measurements. Similarly, the application of the model to remotely sensed ocean color observations revealed strong agreement between satellite-derived estimates of phytoplankton size structure and in situ observations, with a performance comparable to the current regional operational datasets on size structure. The proposed conceptual regional model, parameterized with the most extended in situ pigment dataset available to date for the area, serves as a suitable foundation for long-term (1997–present) analyses on phytoplankton size structure and ecological indicators (i.e., phenology), ultimately linking higher trophic level responses to a changing Mediterranean Sea. Full article

This study analyzed the relationship between zooplankton communities and water quality characteristics, with a focus on total organic carbon (TOC), in 22 reservoirs within the Geum River basin that share similar climatic conditions but exhibit varying levels of pollution. Across all reservoirs, zooplankton community structures showed the highest correlations with TOC, suspended solids (SS), chlorophyll-a (Chl-a), and Secchi depth (SD), with stronger associations observed for rotifers and cladocerans compared to copepods. The classification of zooplankton community composition patterns, followed by an analysis of their associations with TOC concentrations, revealed relatively distinct differences between high-TOC and low-TOC reservoirs, indicating that TOC functions as a key determinant of community composition. Meanwhile, network analysis based on overall water quality characteristics indicated that patterns of water quality similarity among zooplankton-based communities differed somewhat from those based solely on TOC concentrations, suggesting that TOC may exert an independent influence on zooplankton community structure. In high-TOC reservoirs, typical eutrophic characteristics—such as elevated chlorophyll-a, total phosphorus, and suspended solids, along with reduced water transparency—were observed, accompanied by higher zooplankton abundance and a greater proportion of rotifers within the community. In contrast, low-TOC reservoirs, despite exhibiting no marked differences in other water quality variables, showed higher diversity of cladocerans alongside rotifers, further supporting the independent role of TOC in shaping zooplankton community structures. These findings highlight TOC not only as a general indicator of pollution but also as an ecologically significant factor influencing zooplankton community composition and carbon dynamics in reservoir ecosystems. They suggest that TOC should be considered a key variable in future assessments and management of lentic ecosystems. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

To explore the effects of different plant combinations in ecological floating beds on water quality purification and phytoplankton community structure in shallow eutrophic lakes, we conducted a survey of phytoplankton communities within ecological floating beds featuring distinct plant combinations in Meiliang Bay, Lake Taihu, during June and August 2021. The study focuses on two combinations: EA (Canna indica + Acorus calamus + Phragmites australis) and ES (Canna indica + Oenanthe javanica + Sagittaria sagittifolia). Results indicated that ecological floating beds significantly improved water quality, with the strongest restoration effects observed in the EA area. Specifically, turbidity was reduced by 47–89%, while chlorophyll a (Chl-a) concentration inhibition rates reached 82% in June and 54% in August. The comprehensive trophic state index (TLI) remained stable at levels indicating slight eutrophication (≤58.6). Phytoplankton community structure shifted from dominance by eutrophic functional groups (primarily FG M) toward greater diversity. In the EA area, the number of dominant functional groups increased from five (control) to six, and the abundance of the key cyanobacteria group (FG M) declined from 18.29% (control) to 7.86%. Redundancy analysis (RDA) revealed temporal changes in driving factors: nutrients were primary in June (explanation rate: 64.7%), while physical factors dominated in August (explanation rate: 51.2%). This study demonstrates that installing ecological floating beds with diverse plant combinations in shallow eutrophic lakes can effectively alter phytoplankton community structure and enhance in situ water restoration. Among the tested combinations, EA (Canna indica + Acorus calamus + Phragmites australis) exhibited the optimal restoration effect. These findings provide a scientific basis for water environment protection and aquatic biological resource restoration in shallow eutrophic lakes. Full article

This study investigates the effect of high urban land temperatures on pesticide residue (PR) accumulation in cabbage and lettuce and on public health in the Greater Accra Metropolitan Area (GAMA) in Ghana. A comparative toxicological analysis regarding the food system was conducted with 66 farmers across three land surface temperatures: low (Atomic, n = 22), moderate (Ashaiman, n = 22), and high (Korle-Bu, n = 22). Pesticide residue concentrations were assessed using an ANOVA to examine spatial variations across sites. The results indicate a strong correlation between high land surface temperatures and pesticide residue accumulation, with lettuce recording significantly (p < 0.05) higher PR levels than cabbage. Several pesticides, including carbendazim (CBZ), Imidacloprid (IMI), Thiamethoxam (TMX), and Chlorpyrifos (CHL), exceeded the maximum residue limits (MRLs) set by the World Health Organization (WHO) and the European Union (EU) at moderate and high-temperature sites. carbendazim was the dominant pesticide detected, with a concentration of 19.0 mg/kg in lettuce, which far exceeded its maximum residue limit (MRL) of 0.10 mg/kg across all study sites. Statistical analyses (PERMANOVA) confirmed that land surface temperatures and pesticide types significantly influenced the PR concentrations. Public health risk assessments indicate that children are more vulnerable to pesticide exposure than adults. The toxicity hazard quotient (THQ) for organophosphate pesticides, particularly CHL and Dimethoate (DMT), exceeded safe thresholds at moderate and high-temperature sites. Full article

(1) Although lotus ponds exhibit ecological benefits in wetland restoration, their efficacy in water purification and eutrophication mitigation remains unclear. (2) This study utilized Jinluo lotus pond as the experimental group and the adjacent river as the control. Five sampling points were established in each area, with water samples collected in June 2022, April 2025, and May 2025. (3) The pH, BOD, COD, TN, and NH₃-N concentrations in Jinluo lotus pond water are higher than those in rivers, while the TP, NO₃-N, Chl-a, and algal cell density in rivers are higher. However, there was no significant difference in the nine parameters (p > 0.05) in June 2022. The pH, DO, algal cell density, and algal biomass of the Jinluo lotus pond were significantly higher (p < 0.05 for DO); the concentrations of BOD, COD, TN, TP, NH₃-N, NO₃-N, PI, and Chl-a in rivers are higher, with significant differences in Chl-a (p < 0.05) in April 2025. The BOD, COD, TP, NO₃-N, and PI of the Jinluo lotus pond were relatively high (p < 0.05 for PI); the pH, TN, NH₃-N, DO, Chl-a, algal cell density, and algal biomass of rivers are higher, with significant differences in Chl-a (p < 0.05) in May 2025. The results showed that there was no significant difference in the four diversity indicators in June 2022, April 2025, and May 2025. There was no significant difference in the algal diversity indices, including species richness (S), Shannon–Wiener diversity index (H), Simpson diversity index (P), and Pielou evenness index (E) between Jinluo lotus pond and rivers. (4) Conclusions and Recommendations: The Jinluo lotus pond and adjacent rivers suffer from severe nutrient overload, especially with BOD, COD, and TN all being classified as Class 5 water. Expanding natural and constructed reed communities is recommended to enhance nutrient removal. However, given the limited purification capacity of lotus ponds, maintaining or increasing their area may not be justified. Full article

The suitable growth environment for quinoa is high-altitude areas. In recent years, quinoa is also gradually cultivated in other regions with high-temperature exposure. High-temperature stress poses a potential constraint on quinoa quality and yield by impacting pigments, photosynthesis, and metabolites. This study aimed to investigate the effect of exogenous melatonin (MT) in alleviating heat stress on quinoa in controllable conditions. Day/night temperatures were maintained at 35/25 °C in a climate chamber, and foliar spraying was performed using melatonin (MT) concentrations of 0, 50, 100, and 200 μmol L⁻¹. Day/night temperatures were maintained at 25/15 °C in another climate chamber as a comparative trial. Our results demonstrated that high temperature decreased the levels of photosynthetic pigments and the values of photosynthetic rate (P_n), stomatal conductance (g_s), and transpiration rate (T_r). Additionally, it also influenced the accumulation of polyphenols and altered polyphenol oxidase (PPO) activity in the red quinoa (RQ) cultivar. Obvious reductions in gas exchange parameters and metabolites including flavonoid, anthocyanin, and PPO were observed both in the BQ cultivar and the WQ cultivar. However, the application of 100 μmol L⁻¹ MT significantly increased the levels of photosynthetic pigments, the values of P_n, g_s, and T_r, and the PPO activity, as well as the contents of flavonoid and anthocyanin in the RQ cultivar. The application of 50 μmol L⁻¹ MT only led to an increase in the concentrations of Chl a, Chl (a + b), and flavonoids, as well as PPO activity, whereas 100 μmol L⁻¹ MT significantly enhanced the values of P_n, g_s, and T_r and the PPO activity. Additionally, 200 μmol L⁻¹ MT contributed to the synthesis of anthocyanins and polyphenols, and enhanced PPO activity in the BQ cultivar. The application of 50 μmol L⁻¹ MT limited the increase in the contents of total polyphenols, flavonoids, and anthocyanin, we all as PPO activity, in the WQ cultivar. The findings demonstrated that photosynthesis and metabolite synthesis in quinoa under high temperatures depends on an interactive response between cultivar and melatonin levels. The application of 100 μmol L⁻¹ MT was found to be optimal for alleviating the adverse effects of high temperature on photosynthesis and metabolites in the RQ cultivar during actual production. Full article

The selection of environmental variables with different spatial resolutions is a critical factor affecting the accuracy of machine learning-based fishery forecasting. In this study, spring-season survey data of Decapterus macarellus in the South China Sea from 2016 to 2024 were used to construct six machine learning models—decision tree (DT), extra trees (ETs), K-Nearest Neighbors (KNN), light gradient boosting machine (LGBM), random forest (RF), and extreme gradient boosting (XGB)—based on seven environmental variables (e.g., sea surface temperature (SST), chlorophyll-a concentration (CHL)) at four spatial resolutions (0.083°, 0.25°, 0.5°, and 1°), filtered using Pearson correlation analysis. Optimal models were selected under each resolution through performance comparison. SHapley Additive exPlanations (SHAP) values were employed to interpret the contribution of environmental predictors, and the maximum entropy (MaxEnt) model was used to perform habitat suitability mapping. Results showed that the XGB model at 0.083° resolution achieved the best performance, with the area under the receiver operating characteristic curve (ROC_AUC) = 0.836, accuracy = 0.793, and negative predictive value = 0.862, outperforming models at coarser resolutions. CHL was identified as the most influential variable, showing high importance in both the SHAP distribution and the cumulative area under the curve contribution. Predicted suitable habitats were mainly located in the northern and central-southern South China Sea, with the latter covering a broader area. This study is the first to systematically evaluate the impact of spatial resolution on environmental variable selection in machine learning models, integrating SHAP-based interpretability with MaxEnt modeling to achieve reliable habitat suitability prediction, offering valuable insights for fishery forecasting in the South China Sea. Full article

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article