Search Results (2,090)

Protecting aquatic biodiversity while ensuring reliable hydropower production and water supply remains a core challenge for both water security and biosecurity. In this PRISMA-based systematic review, we synthesize evidence from 96 studies on fish guidance and deterrence at hazardous water intakes. We examine non-physical barriers, including acoustic and light cues, electric fields, bubble curtains, and chemical stimuli, as well as physical barriers such as racks, guidance structures, and nets or screens that aim to divert fish away from intakes and toward selective passage routes. Overall, guidance and deterrence performance is strongly species- and site-specific. Multimodal systems that combine multiple cues show the highest mean guidance efficiency (~80%), followed by light-based deterrents (~77%). Acoustic, electric, and bubble barriers generally achieve intermediate efficiencies (~55–58%), whereas structural devices alone exhibit lower mean performance (~46%), with substantial variability among sites and designs. Physical screens remain effective for larger size classes but can increase head loss and debris accumulation. By contrast, non-physical systems offer more flexible, low-footprint options whose success depends critically on local hydraulics, the sensory ecology of target species, and ambient environmental conditions. We identify major knowledge gaps relating to underlying sensory and behavioral mechanisms, hydraulics-based design rules, and standardized performance metrics. We also highlight opportunities to integrate advanced monitoring and AI-based analytics into adaptive, site-specific guidance systems. Taken together, our findings show that carefully selected and tuned barrier technologies can provide practical pathways to enhance water security and biosecurity, while supporting sustainable fish passage, improving invasive-species control, and reducing ecological impacts at water infrastructure. Full article

The rapid shrinkage of the climate-regulating cryosphere, driven by global warming and anthropogenic activities, underscores the urgency of understanding its impact on regional ecological vulnerability. This study develops a Sensitivity–Resilience–Pressure (SRP) model-based framework comprising 21 natural and socio-economic indicators, employs spatial autocorrelation and center of gravity migration to characterize spatiotemporal patterns in the Qilian Mountains region, and integrates Random Forests (RF) with Shapley Additive Explanations (SHAP) to identify key drivers. Results reveal a downward trend in the Ecological Vulnerability Index (EVI) from 2000 to 2020, with areas of very heavy vulnerability declining from 21.05% to 14.73%, indicating gradual ecological recovery. The study area exhibits moderate vulnerability, with the western region dominated by heavy and very heavy vulnerability, whereas the eastern region is characterized by potential and light vulnerability, indicating a high-west, low-east spatial pattern. A significant positive spatial autocorrelation is observed, revealing that areas with high vulnerability are highly clustered and primarily overlap with regions of high elevation and sparse vegetation. The RF–SHAP analysis demonstrates that natural factors dominate the EVI, with fractional vegetation cover, biological abundance, glacial meltwater volume, annual precipitation, and the landscape diversity index emerging as the main drivers, and the EVI changing sequentially as each indicator approaches its threshold: 0.16, 56.57, 2.23 mm, 400.73 mm, and 0.39. In conclusion, although ecological vulnerability in the Qilian Mountains has declined, future management strategies should leverage these threshold effects to implement precise, indicator-based monitoring and regulation. Full article

In the context of climate change, alterations to the physico-chemical properties of soils, particularly in Mediterranean regions, are a growing source of preoccupation. This study analyzes the ecological plasticity and biochemical adaptability of Thymus saturejoides to changes in soil physico-chemical properties in four contrasting environments in Morocco’s western High Atlas (TM: Tidili msfioua, SF: Sti fadma, TA: Taouss, TN: Tisi ntast). It highlights the influence of edaphic characteristics on the physiology and metabolic composition of the species, revealing marked soil heterogeneity between sites. The results for the physico-chemical characteristics of the soil revealed marked heterogeneity between sites. Tisi ntast and Taouss soils had the highest values in terms of electrical conductivity (TN: 0.25 dS/m, TA: 0.18 dS/m), available phosphorus (TN: 18.58 ppm and TA: 26.06 ppm) and total nitrogen (TN: 0.27% and TA: 0.14%), associated with a silty texture, suggesting higher fertility. Conversely, the soil at the TM site was characterized by low total nitrogen content (0.09%), a high C/N ratio (24.4) and a sandy-silty texture, indicating more constraining conditions for plant growth. From a physiological standpoint, plants from the TA site had the lowest chlorophyll levels (17.10 mg g⁻¹FW), while those from the TN site showed the highest levels (31.08 mg g⁻¹FW), accompanied by increased protein content and reduced polyphenol oxidase and peroxidase. In contrast, TM plants showed significant accumulation of total soluble sugars (30 mg g⁻¹FW), proline (22.53 µmol g⁻¹FW), hydrogen peroxide (1.33 nmol g⁻¹FW) and malondialdehyde (62.97 nmol g⁻¹FW), reflecting strong activation of oxidative stress responses. On the other hand, plants from the TA site displayed significantly lower levels of these stress markers compared to other sites, suggesting greater physiological resilience. These results highlight the pivotal role of interactions between edaphic and environmental conditions in modulating plant physiological and biochemical responses, shedding light on the ecological adaptation mechanisms of plant species to the contrasting ecosystems of the Western High Atlas. Full article

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

Endoperoxides constitute a distinctive class of highly oxygenated terpenoids defined by the presence of a cyclic peroxide (–O–O–) bond, a structural motif responsible for their pronounced chemical reactivity and diverse biological effects. Naturally occurring endoperoxide-containing terpenoids are broadly distributed across terrestrial and marine taxa, including higher plants, algae, fungi, and bryophytes, where they are believed to participate in chemical defense and ecological interactions. This review provides a comprehensive overview of naturally occurring endoperoxide terpenoids, focusing on their natural sources, structural diversity, and reported biological activities. Particular emphasis is placed on compounds exhibiting antiprotozoal and antitumor activities, exemplified by artemisinin and its derivatives, which remain cornerstone agents in antimalarial therapy and continue to attract interest for their anticancer potential. Structure–activity relationship (SAR) analysis, supported by computational prediction using the PASS (Prediction of Activity Spectra for Substances) platform, is employed to examine correlations between peroxide-containing frameworks and biological function. Comparative assessment of experimental data and predicted activity profiles identifies key structural features associated with antiprotozoal, antineoplastic, and anti-inflammatory effects. Collectively, this review highlights endoperoxides as a valuable and chemically distinctive class of bioactive natural products and discusses their promise and limitations as leads for further pharmacological development, particularly in light of their intrinsic reactivity and stability challenges. Full article

The Jiangsu Coastal Thermal Front (JCF), a persistent feature in Chinese marginal seas, plays a significant role in modulating phytoplankton dynamics and carbon cycling. However, the multi-scale spatiotemporal variability of the persistent JCF and the underlying mechanisms driving its ecological effects remain limited. Using satellite observations and reanalysis data, this study systematically investigates the JCF’s distribution and its regulatory impact on phytoplankton chlorophyll-a (Chla) and particulate organic carbon (POC). Results show the persistent JCF is most active in summer and winter, primarily in Haizhou Bay and the Jiangsu Shoal. In summer, stratification-induced nutrient limitation within the Haizhou Bay thermal front decreases Chla and POC (by ~−20% and ~−40%, respectively), whereas nutrient-replete non-frontal waters support higher biomass. In the Jiangsu Shoal, the thermal front blocks the southward transport of POC, helping to maintain stable POC levels in the nearshore non-frontal region; meanwhile, the shift from southward to northward transport leaves the offshore non-frontal area without sufficient replenishment, resulting in a ~35% decrease in POC. In winter, the Haizhou Bay thermal frontal barrier effect restricts suspended particulate matter, alleviating light limitation inside the front and enhancing Chla (up to 15%) while reducing POC due to diminished resuspension. We elucidate that the JCF shapes ecological patterns through two primary pathways: by directly acting as a barrier to material transport and by interacting with ancillary processes like upwelling. These findings advance the mechanistic understanding of frontal impacts on coastal ecosystems and provide a mechanistic basis for understanding synergistic coastal carbon sinks. Full article

The invasive perennial weed Ambrosia confertiflora (Burr ragweed) is widespread across various climatic regions in Israel and neighboring countries. This study examines how temperature affects the development of the plants’ aboveground and underground organs, as well as biomass allocation. We hypothesize that temperature influences how the plant distributes resources, thereby modifying its phenotypic morphology and contributing to its spread. Plants were grown in a phytotron under four seasonal temperature regimes (10–16 °C, 16–22 °C, 22–28 °C, 28–34 °C, N-D, 14 h light). We measured above- and belowground biomass, growth form, leaf size, and the interaction between temperature and apical dominance. Our results show that biomass allocation varies with temperature and developmental stage. During early growth, resources are primarily directed toward shoot development and leaf production. As plants matured, they shifted more resources to underground structures, eventually balancing allocation. At lower temperatures, plants invested more in underground growth while the shoot remained in the rosette form. In contrast, higher temperatures favored aboveground growth. Ambrosia confertiflora demonstrates significant phenotypic plasticity in response to temperature variation, affecting plant height, leaf morphology, and resource allocation in both shoot and underground tissues. Understanding how temperature drives these changes is critical to understanding the spread and ecological impact of this highly adaptable weed. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Lake Kotokel is one of the largest lakes along the eastern shore of Lake Baikal. Increasing anthropogenic pressure combined with climate variability led to a sharp decline in its ecological condition, culminating in an outbreak of Gaff disease in 2009. Moreover, Lake Kotokel may serve as a source of pollutants to Lake Baikal due to its hydrological connection via a system of rivers. In light of these factors, ongoing research seeks to identify the drivers of ecological degradation in the lake ecosystem and provide a comprehensive assessment of its current environmental status and potential adverse processes. In this study, we report, for the first time, the occurrence of microplastic particles in the surface water and sediments of Lake Kotokel. Mean microplastic concentrations were 0.59 ± 0.25 items/m³ in water and 280 ± 162 items/kg dry weight in sediments, with corresponding average microplastic masses of 2.6 ± 1.4 μg/m³ in water and 1.33 ± 1.21 mg/kg dry weight in sediments. In surface waters, microplastic were predominantly 1–3 mm in size, whereas sediments were dominated by 0.1–1 mm particles. Fibers were the most abundant morphological form, comprising 79.5% and 82.8% of particles in water and sediments, respectively. Five polymer types were identified, with polyethylene terephthalate being the most prevalent. Based on particle abundance and polymer composition, the ecological risk of microplastic in Lake Kotokel was assessed as low to moderate. Full article

Understanding the variations in twig and leaf morphologies and photosynthetic physiological characteristics of riparian forest plants in heterogeneous habitats is of great significance for revealing their phenotypic plasticity mechanisms and ecological adaptation strategies. In this study, the riparian forest plant Periploca sepium Bunge was selected as the research object. According to the canopy light environment experienced by the P. sepium population, three habitats were established: under-canopy, gap, and full-sun areas. We studied the twig and leaf morphological and photosynthetic characteristics of P. sepium under heterogeneous light environments, as well as the relationships between these two aspects. Plants in the under-canopy area developed long and thick twigs with few large leaves, coupled with high actual photosynthetic efficiency of photosystem II (Y(II)) and low non-photochemical quenching (NPQ), whereas those in the full-sun area exhibited the opposite covariation strategy. Significant correlations between twig and leaf morphologies and photosynthetic physiological characteristics were found across all habitats. The coordinated variations in twig and leaf morphologies and photosynthetic physiology of P. sepium embody a resource investment trade-off strategy that plants have evolved through long-term adaptation to heterogeneous light environments. Full article

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies. Full article

Ecological flow management is important for maintaining ecosystem stability and promoting sustainable development. Dynamic ecological flow regulation depends on precise real-time monitoring of water levels and flow velocities. To address challenges in ecological flow monitoring, including maintenance difficulties and insufficient accuracy, an improved DSC-YOLOv8n-seg model is proposed for dynamic multi-parameter sensing, achieving more efficient object detection and semantic segmentation. Compared with traditional affine transformation-edge detection, this approach enables joint recognition of water level lines and staff gauge characters, achieving an average recognition error of ±1.2 cm, with a model accuracy of 93.1%, recall rate of 94.5%, and mAP50:95 of 93.9%. A deep learning-based spectral principal direction recognition method was also employed to calculate the surface water flow velocity, which demonstrated stable and efficient performance, achieving a relative error of 0.005 m/s for the surface velocity. Experimental results confirm that it can effectively address issues such as environmental interference, exhibiting enhanced robustness in low-light and nighttime scenarios. The proposed method provides efficient and accurate identification for dynamic water level monitoring and for real-time detection of river surface flow velocities to improve ecological flow management. Full article

Morningglories (Ipomoea lacunosa, I. hederacea, and I. purpurea) are persistent, problematic weeds in summer row crops throughout warm-temperate regions. Their vining growth habit and enduring seedbanks lead to recurring infestations and harvest interferences. This review synthesizes current knowledge on the seed ecology of these species to clarify how dormancy, germination, and emergence processes contribute to their persistence. Published anatomical and ecological studies were examined to summarize dormancy mechanisms, environmental signals regulating dormancy release, germination requirements, and seasonal emergence patterns. Morningglories exhibit a dormancy system dominated by physical dormancy, occasionally combined with a transient physiological component. Dormancy release is promoted by warm and fluctuating temperatures, hydration–dehydration cycles, and long-term seed-coat weathering. Once permeable, seeds germinate across broad temperature ranges, vary in sensitivity to water potential, and show limited dependence on light. Field studies indicate extended emergence windows from late spring through midsummer, especially in no-till systems where surface seeds experience strong thermal and moisture fluctuations. Despite substantial progress, significant gaps remain concerning maternal environmental effects, population-level variation, seedbank persistence under modern management, and the absence of mechanistic emergence models. An improved understanding of these processes will support the development of more predictive and ecologically informed management strategies. Full article

Saxitoxin (STX) is one of the most potent marine neurotoxins, produced by several species of freshwater cyanobacteria and marine dinoflagellates. Although omics-based approaches have advanced our understanding of STX biosynthesis in recent decades, the origin, regulation, and ecological drivers of STX in dinoflagellates remain poorly resolved. Specifically, dinoflagellate STX biosynthetic genes (sxt) are extremely fragmented, inconsistently expressed, and unevenly distributed between toxic and non-toxic taxa. Environmental studies further report inconsistent relationships between abiotic factors and STX production, suggesting regulation across multiple genomic, transcriptional, post-transcriptional, and epigenetic levels. These gaps prevent a comprehensive understanding of STX biosynthesis in dinoflagellates and limit the development of accurate predictive models for harmful algal blooms (HABs) and paralytic shellfish poisoning (PSP). Artificial intelligence (AI), including machine learning and deep learning, offers new opportunities in ecological pattern recognition, molecular annotation, and data-driven prediction. This review explores the current state of knowledge and persistent knowledge gaps in dinoflagellate STX research and proposes an AI-integrated multi-omics framework highlighting recommended models for sxt gene identification (e.g., DeepFRI, ProtTrans, ESM-2), evolutionary reconstruction (e.g., PhyloGAN, GNN, PhyloVAE, NeuralNJ), molecular regulation (e.g., MOFA+, LSTM, GRU, DeepMF), and toxin prediction (e.g., XGBoost, LightGBM, LSTM, ConvLSTM). By integrating AI with diverse biological datasets, this novel framework outlines how AI can advance fundamental understanding of STX biosynthesis and inform future applications in HAB monitoring, seafood safety, and PSP risk management in aquaculture and fisheries. Full article

From an ecological perspective, sustainable lighting in urban marine areas requires striking a balance between meeting human needs and protecting marine ecosystems from the harmful effects of disrupting natural light regimes. While managing artificial lighting is crucial, we argue that preventing obstructions to light penetration into the water is as important, as many marine organisms depend on the euphotic zone. This study intends to review the key factors for the implementation of wildlife-friendly lighting design in the urban marine environment, as the subject is explored in ecological studies but scarcely discussed in urban studies. An integrative literature review and cases are employed to synthesise evidence about changes to the light regime in urban marine areas from four perspectives: light reach, intensity, spectrum, and duration. The cases present measures implemented to benefit marine species affected by alterations to the light regime following urbanisation in a way that they could still thrive in a modified built environment. In discussion, it is acknowledged that achieving sustainable lighting in urban marine areas is a multifaceted challenge involving concurrent influencing factors, including a shared agency between humans and non-humans, which may require comprehensive lighting designs that are tailored to specific goals and target species. Full article