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Keywords = ecological early warning indicator

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19 pages, 10111 KB  
Article
Threshold Extraction and Early Warning of Key Ecological Factors for Grassland Degradation Risk
by Jingbo Li, Wei Liang, Min Xu, Haijing Tian, Xiaotong Gao, Yujie Yang, Ruichen Hu, Yu Zhang and Chunxiang Cao
Remote Sens. 2025, 17(17), 3098; https://doi.org/10.3390/rs17173098 - 5 Sep 2025
Viewed by 860
Abstract
Grassland degradation poses a serious threat to ecosystem stability and the sustainable development of human societies. In this study, we propose a framework for grassland degradation risk assessments and early warning based on key ecological factors (KEFs) in Xilingol. The NDVI, NPP, and [...] Read more.
Grassland degradation poses a serious threat to ecosystem stability and the sustainable development of human societies. In this study, we propose a framework for grassland degradation risk assessments and early warning based on key ecological factors (KEFs) in Xilingol. The NDVI, NPP, and grass yield were selected as KEFs to represent vegetation coverage, ecosystem productivity, and actual biomass, respectively. By constructing a grassland degradation index (GDI) and integrating K-means clustering, the average curvature, and a gravity center shift analysis, we quantified the degradation risk levels and identified the threshold values for different grassland types. The results showed the following: (1) the grass yield was the most sensitive indicator of grassland degradation in Xilingol, with high-risk thresholds decreasing from 115.67 g·m−2 in the temperate meadow steppes (TMSs) to 73.27 g·m−2 in the temperate typical steppes (TTSs), and further to 32.30 g·m−2 in the temperate desert steppes (TDSs); (2) the TDSs exhibited the highest curvature value (2.81 × 10−4) in the initial stage, indicating a higher likelihood of rapid early-stage degradation, whereas the TMSs and TTSs reached peak curvature in the latest stages; and (3) the TTSs had the largest proportion of high-risk areas (33.02%), with a northeast–southwest distribution and a probable westward expansion trend. This study provides a practical framework for grassland degradation risk assessments and early warning, offering valuable guidance for ecosystem management and sustainable land use. Full article
(This article belongs to the Special Issue Remote Sensing in Applied Ecology (Second Edition))
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20 pages, 9798 KB  
Article
Spatiotemporal Risk Assessment of H5 Avian Influenza in China: An Interpretable Machine Learning Approach to Uncover Multi-Scale Drivers
by Xinyi Wang, Yihui Xu and Xi Xi
Animals 2025, 15(16), 2447; https://doi.org/10.3390/ani15162447 - 20 Aug 2025
Viewed by 512
Abstract
Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse [...] Read more.
Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation R2 = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. Full article
(This article belongs to the Section Veterinary Clinical Studies)
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17 pages, 3027 KB  
Article
Time Series Prediction of Water Quality Based on NGO-CNN-GRU Model—A Case Study of Xijiang River, China
by Xiaofeng Ding, Yiling Chen, Haipeng Zeng and Yu Du
Water 2025, 17(16), 2413; https://doi.org/10.3390/w17162413 - 15 Aug 2025
Viewed by 727
Abstract
Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang [...] Read more.
Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R2 > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R2 improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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23 pages, 4722 KB  
Article
Spatial and Temporal Inconsistency of Forest Resilience and Forest Vegetation Greening in Southwest China Under Climate Change
by Lu Cai, Yining Luo, Yan Lan, Guoxiang Shu, Denghong Huang, Zhongfa Zhou and Lihui Yan
Plants 2025, 14(16), 2493; https://doi.org/10.3390/plants14162493 - 11 Aug 2025
Viewed by 497
Abstract
Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, [...] Read more.
Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, combined with BEAST and Random Forest methods, to quantify and analyze the spatiotemporal evolution of forest resilience and its driving factors in Southwest China from 2000 to 2022. The results show the following: (1) Forest resilience exhibits a “high in the northwest and low in the southeast” spatial distribution, with a temporal pattern of “increase-decrease-increase.” The years 2010 and 2015 are key turning points. Trend shift analysis divides resilience into six types. (2) Although forest vegetation shows a clear greening trend, resilience does not necessarily increase with greening, and in some areas, an “increase in greening—decline in resilience” asynchronous pattern appears. (3) The annual average temperature, precipitation, and solar radiation are the main climate factors and their influence on resilience follows a nonlinear relationship. Higher temperatures and increased radiation may suppress resilience, while increased precipitation can enhance it. This study suggests incorporating the TAC indicator into ecological monitoring and early warning systems, along with applying trend classification results for region-specific management to improve the scientific basis and adaptability of forest governance under climate change. Full article
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24 pages, 9190 KB  
Article
Modeling the Historical and Future Potential Global Distribution of the Pepper Weevil Anthonomus eugenii Using the Ensemble Approach
by Kaitong Xiao, Lei Ling, Ruixiong Deng, Beibei Huang, Qiang Wu, Yu Cao, Hang Ning and Hui Chen
Insects 2025, 16(8), 803; https://doi.org/10.3390/insects16080803 - 3 Aug 2025
Viewed by 749
Abstract
The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add [...] Read more.
The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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28 pages, 6962 KB  
Article
Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation
by Aikaterini Stamou, Aikaterini Bakousi, Anna Dosiou, Zoi-Eirini Tsifodimou, Eleni Karachaliou, Ioannis Tavantzis and Efstratios Stylianidis
Land 2025, 14(8), 1564; https://doi.org/10.3390/land14081564 - 30 Jul 2025
Viewed by 2235
Abstract
The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are [...] Read more.
The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article
(This article belongs to the Special Issue Land and Drought: An Environmental Assessment Through Remote Sensing)
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22 pages, 2461 KB  
Article
Environmental Drivers of Phytoplankton Structure in a Semi-Arid Reservoir
by Fangze Zi, Tianjian Song, Wenxia Cai, Jiaxuan Liu, Yanwu Ma, Xuyuan Lin, Xinhong Zhao, Bolin Hu, Daoquan Ren, Yong Song and Shengao Chen
Biology 2025, 14(8), 914; https://doi.org/10.3390/biology14080914 - 22 Jul 2025
Viewed by 548
Abstract
Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental [...] Read more.
Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article
(This article belongs to the Special Issue Wetland Ecosystems (2nd Edition))
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25 pages, 11278 KB  
Article
Analysis of Droughts and Floods Evolution and Teleconnection Factors in the Yangtze River Basin Based on GRACE/GFO
by Ruqing Ren, Tatsuya Nemoto, Venkatesh Raghavan, Xianfeng Song and Zheng Duan
Remote Sens. 2025, 17(14), 2344; https://doi.org/10.3390/rs17142344 - 8 Jul 2025
Viewed by 712
Abstract
In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is [...] Read more.
In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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16 pages, 10263 KB  
Article
Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model
by Xiaorui Zhang, Haiwen Wang and Tingting Dai
Agriculture 2025, 15(13), 1411; https://doi.org/10.3390/agriculture15131411 - 30 Jun 2025
Viewed by 629
Abstract
Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis [...] Read more.
Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of P. cinnamomi under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). The results indicate that currently suitable habitats cover the majority of China’s provinces (>50% of their areas), with only sporadic low-suitability zones in Qinghai, Tibet, and Xinjiang. The most influential environmental variables were the mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Under future climate scenarios, new suitable habitats emerged in high-latitude regions, while the highly suitable area expanded significantly, with the distribution centroid shifting northeastward. This study employs predictive modeling to elucidate the future distribution patterns of P. cinnamomi in China, providing a theoretical foundation for establishing a regional-scale disease early warning system and formulating ecological management strategies. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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22 pages, 2974 KB  
Article
Determination of Soft Partitioning Thresholds for Reservoir Drought Warning Levels Under Socio-Hydrological Drought
by Yewei Liu, Xiaohua Xu, Rencai Lin, Weifeng Yang, Peisheng Yang, Siying Li and Hongxin Wang
Agriculture 2025, 15(13), 1408; https://doi.org/10.3390/agriculture15131408 - 30 Jun 2025
Viewed by 466
Abstract
The failure of traditional drought indices to capture the dynamic supply–demand imbalance in socio-hydrological systems hinders proactive water management and necessitates novel assessment frameworks. The reservoir drought warning water level, serving as a dynamic threshold indicating supply–demand imbalance, provides a critical basis for [...] Read more.
The failure of traditional drought indices to capture the dynamic supply–demand imbalance in socio-hydrological systems hinders proactive water management and necessitates novel assessment frameworks. The reservoir drought warning water level, serving as a dynamic threshold indicating supply–demand imbalance, provides a critical basis for drought early warning. From a socio-hydrological drought perspective, this study develops a framework for determining staged and graded soft partition thresholds for reservoir drought warning water levels, encompassing three key stages: water stress analysis, phase classification, and threshold determination. First, water demands for the ecological, agricultural, and domestic sectors were quantified based on hydrological analysis and official operational rules. Second, an optimized KPCA-Fisher model delineated the intra-annual supply–demand dynamics into distinct periods. Thirdly, the soft partition thresholds were formulated by coupling these multi-sectoral demands with water deficit rates using a triangular membership function. Applied to the Xianan Reservoir, the framework yielded distinct drought warning thresholds for the identified main flood, critical demand, and dry seasons. Validation against historical droughts (2019 and 2022) confirmed that these soft thresholds more accurately tracked the drought evolution process compared to traditional hard partitions. Furthermore, a sensitivity analysis identified the ecological water demand methodology as a key factor influencing the thresholds, particularly during the critical demand period. The proposed framework for determining staged and graded reservoir drought warning water levels better reflects the complexity of socio-hydrological systems and provides a scientific basis for refined reservoir drought early warnings and management under changing environments. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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22 pages, 7115 KB  
Article
Identification and Feeding Characterization of Sterkiella histriomuscorum (Protozoa, Ciliophora, Hypotrichia) Isolated from Outdoor Mass Culture of Scenedesmus dimorphus
by Mengyun Wang, Pei Chen, Hongxia Wang, Qiong Deng, Xiaonan Zhang, Guoqing Yuan, Mixue Jiang, Lingling Zheng, Zixuan Hu, Zemao Gu, Denis V. Tikhonenkov and Yingchun Gong
Microorganisms 2025, 13(5), 1016; https://doi.org/10.3390/microorganisms13051016 - 28 Apr 2025
Viewed by 658
Abstract
Herbivorous protistan grazers are ubiquitous and abundant in marine and temperate freshwater environments. However, little is known about the algivorous ciliates and their feeding habits in outdoor mass algal cultures. In this study, we report on one hypotrich ciliate, identified as Sterkiella histriomuscorum [...] Read more.
Herbivorous protistan grazers are ubiquitous and abundant in marine and temperate freshwater environments. However, little is known about the algivorous ciliates and their feeding habits in outdoor mass algal cultures. In this study, we report on one hypotrich ciliate, identified as Sterkiella histriomuscorum, from the outdoor mass culture of Scenedesmus in Arizona, USA. A long-term field survey revealed that this species often occurs in Scenedesmus culture in spring and summer, and can graze very heavily on Scenedesmus cells. By isolating Sterkiella cells and then observing them via light microscopy and electron microscopy, detailed information about the morphology, ultrastructure, excystment process, and feeding characteristics of the ciliate was obtained. Specifically, it seems that S. histriomuscorum has a range of different strategies for excystment, and the sharp change in the ion concentration in the environment around the cyst results in osmotic shock, which likely facilitates the excystment. Feeding experiments revealed that S. histriomuscorum preferred to graze on chlorophytes as well as the diatom Phaeodactylum tricornutum and had no interaction with chrysophytes or cyanobacteria. Molecular phylogenetic analysis based on the SSU rRNA gene sequence indicated that both the genus Sterkiella and the species S. histriomuscorum are non-monophyletic. The information obtained from this study will help advance our understanding of the biodiversity and ecological function of S. histriomuscorum, and will also be very useful in the development of early warning systems and control measures for preventing or treating this contaminant in microalgal mass cultures. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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19 pages, 5116 KB  
Article
Prediction of Shallow Landslide Runout Distance Based on Genetic Algorithm and Dynamic Slicing Method
by Wenming Ren, Wei Zhou, Zhixiao Hou and Chuan Tang
Water 2025, 17(9), 1293; https://doi.org/10.3390/w17091293 - 26 Apr 2025
Viewed by 758
Abstract
Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this [...] Read more.
Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model’s effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model’s reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use. Full article
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30 pages, 442 KB  
Review
Oxidative Stress Biomarkers in Fish Exposed to Environmental Concentrations of Pharmaceutical Pollutants: A Review
by Lăcrămioara Grădinariu, Mirela Crețu, Camelia Vizireanu and Lorena Dediu
Biology 2025, 14(5), 472; https://doi.org/10.3390/biology14050472 - 25 Apr 2025
Cited by 6 | Viewed by 1951
Abstract
Pharmaceutical residues are a result of human activities and are increasingly recognized as environmental contaminants that pose significant risks to aquatic ecosystems. There are many well-known pathways (agricultural runoff, veterinary use, human excretion, etc.) for the entry of these pharmaceuticals into the aquatic [...] Read more.
Pharmaceutical residues are a result of human activities and are increasingly recognized as environmental contaminants that pose significant risks to aquatic ecosystems. There are many well-known pathways (agricultural runoff, veterinary use, human excretion, etc.) for the entry of these pharmaceuticals into the aquatic environment, and among them, the inability to remove these biologically active compounds from wastewater treatment plant (WWTP) effluents is becoming increasingly significant in the context of societal evolution. Once introduced, pharmaceuticals persist at low concentrations, exerting sub-lethal effects that disrupt the physiological processes of aquatic organisms. Among these effects, oxidative stress (OS) has gained attention as a key mechanism that is induced by pharmaceutical toxicity, serving as a sentinel indicator of homeostatic disturbance. Thus, studying OS biomarkers in fish is crucial for understanding the extent of pharmaceutical pollution, as these biomarkers provide early warning signals of environmental stress and help assess sub-lethal impacts on aquatic organisms. Their application, correlated with other eco-physiological investigations, can improve ecological risk assessments. In this context, this review explores the role of OS biomarkers by assessing the effects of pharmaceutical contaminants on fish. It highlights the utility and limitations of these biomarkers for environmental monitoring, while also identifying key research gaps—particularly regarding long-term ecological consequences. Full article
(This article belongs to the Section Toxicology)
15 pages, 2384 KB  
Article
A Dissolved Oxygen Prediction Model for the Yangtze River Basin Based on VMD-IFOA-Attention-GRU
by Zhengyu Zhu and Shouqi Cao
Water 2025, 17(9), 1278; https://doi.org/10.3390/w17091278 - 25 Apr 2025
Viewed by 538
Abstract
Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), [...] Read more.
Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), as a critical indicator for measuring water self-purification capacity and ecological health status, has been widely applied in water quality monitoring and early warning systems. Therefore, accurate prediction of dissolved oxygen concentration is of significant importance for the ecological and environmental protection of river basins. This study introduces a hybrid prediction model combining Variational Mode Decomposition (VMD), Improved Fruit Fly Optimization Algorithm (IFOA), and Attention-based Gated Recurrent Unit (Attention-GRU). The model first decomposes preprocessed dissolved oxygen data through VMD to extract multiple intrinsic mode functions, reducing non-stationarity and high-frequency noise interference. It then utilizes the Improved Fruit Fly Optimization Algorithm to adaptively optimize key parameters of the Attention-GRU network, enhancing the model’s fitting capability. Experiments demonstrate that the VMD-IFOA-Attention-GRU model achieves 0.286, 0.302, and 0.915 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2), respectively, significantly outperforming other comparative models. The results indicate that this method can provide a reference for intelligent water quality prediction in typical regions such as the Yangtze River Basin. Full article
(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water)
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16 pages, 1590 KB  
Article
Environmental Effects on the Ecological Carrying Capacity of Marine Ranching in the Northern South China Sea
by Ziwen Wang, Lijun Yao, Jing Yu, Yuxiang Chen, Xue Feng and Pimao Chen
Biology 2025, 14(4), 419; https://doi.org/10.3390/biology14040419 - 14 Apr 2025
Cited by 1 | Viewed by 650
Abstract
The marine ecological carrying capacity (MECC) of marine ranching serves as a crucial indicator for assessing the conservation effect of fishery resources and forms a significant basis for scientific management of coastal fisheries. The environmental impacts on the MECC of marine ranching in [...] Read more.
The marine ecological carrying capacity (MECC) of marine ranching serves as a crucial indicator for assessing the conservation effect of fishery resources and forms a significant basis for scientific management of coastal fisheries. The environmental impacts on the MECC of marine ranching in the northern South China Sea were analyzed quantitatively by employing Generalized Additive Models (GAMs), which have been successfully applied to the study of the relationship between fishery resources and environmental factors, and factor analysis, using satellite and survey observations. Results showed that 95.40% of the total variation in MECC was explained by these factors. Based on the GAMs, the most important factor was Year (calendar years), with a contribution of 66.20%, followed by Chlorophyll a concentration (Chl-a), Sea Surface Temperature (SST), Dissolved Inorganic Nitrogen (DIN) and Water Current, with contributions of 20.60%, 4.40%, 3.60%, and 0.60%, respectively. The findings of this study inspire us to establish a long-term marine ranching resource and environment monitoring platform, and an early warning and forecasting expert decision-making system, providing scientific references for planning and management of coastal marine ranching. Full article
(This article belongs to the Section Ecology)
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