Search Results (40,160)

Introduction: The European catfish (Silurus glanis) has expanded rapidly across Europe, significantly impacting native freshwater biodiversity. Despite its well-documented ecological and economic effects as a top predator, reproductive biology data from non-native populations remain scarce, limiting the development of effective management strategies. Objective: This study examines key reproductive traits, sex ratio, size at first maturity, spawning period, fecundity, and oocyte diameter, of an invasive European catfish population in the Lower Tagus River (LTR), Portugal, approximately 15 years after its establishment. Methodology: A total of 674 individuals were collected monthly from January 2022 to November 2023 using electrofishing, gill nets, baited hook-lines, and catches from professional fishermen. Sex and reproductive stage were assessed via gonadal analysis. Size at first maturity was estimated using logistic regression. Fecundity was determined by the gravimetric method, and oocyte stage and diameter were assessed histologically. The gonadosomatic index (GSI) was used to characterise the reproductive cycle. Results: The sex ratio was significantly female-biased (1.4:1). Size at first maturity (TL₅₀) was 72.9 cm TL for females and 68.8 cm TL for males. The spawning season extended from February to June, coinciding with water temperatures of 11–23 °C, with the highest GSI values reported to date for this species (GSI max = 22.5%). Histological analysis confirmed asynchronous oocyte development. Absolute fecundity ranged from 8364 to 319,000 oocytes per female and was positively correlated with total length and body weight. Mean mature oocyte diameter ranged from 1.50 to 3.21 mm. Conclusions: The European catfish in the LTR exhibits high reproductive plasticity, early maturity, a prolonged spawning season, and elevated fecundity, likely facilitated by warm water temperatures and abundant prey resources. Crucially, these parameters reveal earlier maturation and greater reproductive investment relative to native populations, demonstrating an extreme phenotypic plasticity characteristic of successful invasions in southern European aquatic ecosystems. These findings provide essential biological parameters for targeted management, including selective removal of large females, intensified fishing effort during the spawning season, and population monitoring to prevent compensatory reproductive responses. Full article

Long-term ichthyoplankton time series provide an effective framework for understanding how marine communities respond to environmental variability across temporal scales. We analyzed larval fish assemblage dynamics in the central Mexican Pacific under contrasting seasonal hydrographic conditions and ENSO phases using multivariate analyses, indicator species analysis, clustering, and generalized additive models. Environmental variability exhibited a hierarchical structure, with recurrent seasonal changes in sea surface temperature (SST) and coastal upwelling intensity (CUI), whereas the Oceanic Niño Index (ONI) varied mainly at the interannual scale. Significant differences in assemblage composition were detected among ENSO–seasonality regimes. Distance-based redundancy analysis showed that the primary compositional gradient was associated with seasonal hydrography, while secondary variation reflected ENSO-related interannual shifts. Species responses were expressed primarily through shifts in relative dominance rather than wholesale species replacement, indicating that assemblage reorganization was largely driven by changes in the relative contribution of recurrent taxa. This pattern highlights the role of seasonal hydrography as the primary environmental filter structuring the assemblage, whereas ENSO variability acts mainly as a secondary modulator of species dominance and community trajectories. Consequently, interannual climate anomalies influenced the relative importance of species without substantially redefining the underlying species pool. These findings improve the understanding of plankton community responses to climate variability in the tropical eastern Pacific. Full article

This study investigated the influences of geographical origin, harvest season, and plucking position on the chemical composition and flavor characteristics of Hainan Dayezhong black tea. Systematic analysis of basic components, volatile profiles and metabolomes of tea samples collected under different pre-harvest conditions revealed significant variations in polyphenols, flavonoids and catechins, as well as distinct differences in volatile composition. Key aroma-active compounds identified were nerolidol, linalool, benzaldehyde, benzeneacetaldehyde and methyl salicylate, which were determined to be decisive for the characteristic aroma profile of Dayezhong black tea. Untargeted metabolomics further demonstrated that these factors do not merely alter individual metabolite levels, but also reprogram energy metabolism, carbon–nitrogen allocation, and secondary metabolic pathways, resulting in distinct metabolic signatures among samples. From a systematic chemical perspective, this study elucidates the metabolic basis of Hainan Dayezhong black tea quality formation and establishes a scientific foundation for targeted quality optimization through regulation of key components. Full article

The unprecedented 2022 wildfire season in Navarra, northern Spain, marked a turning point in regional wildfire management, when seven simultaneous large fires during a June heatwave burned more than 17,000 ha in just a few days, overwhelming suppression capacity and highlighting the limits of a strategy based primarily on ignition prevention and fire suppression. In this study, we implemented a stochastic wildfire modeling system based on the Minimum Travel Time algorithm, historical ignition patterns, spatial fuel data, and spatiotemporal weather variability to assess community and protected area exposure to wildfire. We simulated more than 50,000 fire season replicates under extreme fire weather conditions, estimating annual burn probability across fire intensity classes at 50 m spatial resolution. We then intersected modeled fire perimeters with building footprints representing residential and industrial structures, as well as protected areas, to assess the spatial distribution of exposure across the region. Results showed strong concentration of community exposure, with three fourths of residential and industrial exposure concentrated in just over one third of the total municipal area. Across Navarra, mean annual modeled exposure summed to 120 residential buildings and 16 industrial structures. Across the protected area network, mean annual burned area summed to 90 ha year⁻¹, including 68 ha year⁻¹ at flame lengths greater than 2.5 m, while burned forest area was 16 ha year⁻¹. Protected areas in southern Navarra and forested protected areas in central and northern Navarra showed the highest modeled exposure, identifying priority landscapes where prevention, restoration, and evaluation of managed fire options could support more resilient ecosystems. This study provides a scientific basis for improving wildfire risk governance and strengthening the resilience of communities and protected areas under increasing wildfire pressure in the region. Full article

The influenza A virus (IAV) has been responsible for multiple seasonal epidemics and poses a pandemic threat, and the growing number of variant strains constitutes a persistent threat to humanity. This study aimed to identify anti-influenza compounds from a traditional Chinese medicine (TCM) monomer library using a machine learning approach, with calmodulin as a hypothesis-driven target. The antiviral efficacy of the compounds with the highest predicted binding scores from virtual screening was evaluated using integrated computational and experimental approaches. A pre-trained protein language model (ConPLex) was employed for virtual screening. Molecular docking was used to predict binding characteristics, and network pharmacology was applied to generate hypotheses on multi-target mechanisms. The cytotoxicity and anti-H1N1 activity of the selected compounds were assessed in vitro, followed by in vivo evaluation of survival, lung pathology, viral load, and inflammatory mediators in a lethal mouse infection model. Sodium deoxycholate (NaDC) and deoxycholic acid (DCA) were identified as promising lead compounds. Both exhibited dose-dependent inhibition of viral replication in vitro with low cytotoxicity. Treatment with NaDC and DCA significantly improved survival rates and reduced lung pathology in H1N1-infected mice. Treatment was associated with suppression of nuclear factor kappa-B (NF-κB) activation, reduced pro-inflammatory cytokines, and elevated interleukin-10 (IL-10) levels. Molecular docking predictions indicated that NaDC and DCA exhibit moderate binding affinity for calmodulin, with binding energies of −8.38 kcal/mol and −7.61 kcal/mol, respectively. Furthermore, network pharmacology analysis suggested that these compounds may modulate pathways related to viral infection, inflammation, and immune regulation. NaDC and DCA demonstrate anti-influenza activity both in vitro and in vivo, reducing viral replication and alleviating inflammatory lung injury. These findings position NaDC and DCA as promising lead compounds for anti-influenza drug development and provide a foundation for further mechanistic validation. Full article

In this work, we inspected the friendship network on Twitter (recently rebranded as X), concentrating on individuals and organizations intertwined with the energy field. We particularly focus on seasoned professionals, corporate entities, and domain specialists, all connected through ‘following’ relationships. By meticulously examining these ties, we uncover several distinct groupings within the network, each defined by the unique roles its members occupy. Our analysis demonstrates that the natural emergence of such clusters on social platforms exerts a profound influence on public discourse regarding energy and other critical matters, including climate change. Furthermore, we observe that the resulting communities exhibit distinct structural properties and communication patterns, with some clusters showing lower internal engagement, which may be indicative of fragmentation dynamics in online conversations. These emergent clusters, characterized by their shared communication styles, form relatively compact communities where the exchange of information is infrequent compared to larger networks and is usually confined to accounts created for specific commercial objectives. We emphasize that our analysis focuses on a structurally coherent connected component emerging from a curated set of energy-related seed accounts, rather than attempting to reconstruct the entirety of the energy discourse on Twitter. Consequently, peripheral or weakly connected communities may be underrepresented. Additionally, by combining machine-learning-based node classification with graph-based centrality measures, we are able to characterize the roles of structurally central actors within these niche segments and analyze the connectivity patterns that define their positions. This method provides novel insights into how corporate communication unfolds on social media, offering a refreshed perspective on professional networking. Ultimately, our findings highlight the ways in which companies within the energy sector take advantage of Twitter to coordinate their initiatives, with key institutions serving as central nodes in maintaining the organization of these networks. Full article

This study investigates the population dynamics and seasonal reproductive patterns of Gambusia holbrooki, an invasive fish threatening biodiversity within arid springs of the Edgbaston Spring complex in Queensland, Australia. Using daily aging techniques, we uncover critical life history traits that inform targeted species management. Our findings reveal marked sex-specific mortality rates, with males exhibiting higher mortality than females, a pattern consistent with findings from Tasmania. Reproductive activity peaks were observed between September and November, but persisted throughout the year, excluding January and April of 2020, likely due to elevated water temperatures during these months. Growth modeling identified the power function as the best fit for describing G. holbrooki growth trajectories. These insights highlight the importance of seasonally informed control strategies to mitigate the ecological impact of this pest species. The study provides essential data to support conservation efforts and guide effective management of invasive fish in fragile arid spring ecosystems. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

In many orographically complex monsoon basins, rain gauge networks are sparse and lack the long-term continuous records required for reliable precipitation probability analysis. Traditional regional frequency analysis assumes spatially uniform precipitation across the analysis zone, which is inadequate for basins with steep rainfall gradients and strong seasonal variability. Gridded precipitation products (GPPs) provide spatially continuous, long-term records that enable grid-cell-level probability distribution fitting. However, GPPs may exhibit local biases and errors, and statistical evaluation against gauge observations is necessary before application. This study was conducted in the Phetchaburi–Prachuap Khiri Khan River Basin, western Thailand, a region with steep orographic and coastal rainfall gradients. Four GPPs, namely CHIRPS, CHELSA, WorldClim, and PERSIANN-CCS-CDR, were evaluated against gauge observations. The best-performing product, after monthly bias correction, was then used to generate spatially continuous monthly exceedance probability maps using grid-cell gamma distribution fitting. CHELSA showed the best overall performance across all evaluation metrics (correlation coefficient (r) = 0.908, percent bias (PBIAS) = 7.0%, root mean square error (RMSE) = 48.3 mm), passing the Kolmogorov–Smirnov (KS) goodness-of-fit test at all 96 station-months. CHIRPS and WorldClim showed satisfactory overall performance but exhibited localized biases in complex terrain, whereas PERSIANN-CCS-CDR substantially overestimated wet-season rainfall, limiting its suitability for this basin. Spatial precipitation patterns varied markedly between monsoon regimes, shifting from a dominant west-to-east orographic gradient during the southwest monsoon to a less differentiated advective pattern during the northeast monsoon. Furthermore, analysis at the 75% exceedance probability level showed that mean-based effective rainfall overestimated reliable water supply in high-variance months, leading to underestimation of supplemental irrigation demand. The generated maps provide spatially explicit dependable rainfall estimates across the basin, supporting probabilistic agricultural water management at multiple planning scales in orographically complex monsoon basins. Full article

Optimization of nitrogen (N) management is critical for enhancing maize (Zea mays L.) productivity while maintaining soil health. The present study investigated the impact of split-application fertilization strategies on soil chemical properties and grain yield across three distinct soil types (calcaric fluvisol, luvic phaeozem, and stagnic phaeozem) in Mureș County, Romania, over three cropping seasons (2022–2024). Three fertilization variants were evaluated: the first treatment, designated V1, involved the application of 300 kg/ha NPK 20-20-0 + 300 kg/ha urea, the second treatment V2 utilized 300 kg/ha NPK 20-20-0 + 300 kg/ha NAC 27 N-calcium ammonium nitrate, and the third treatment V3 served as the baseline control, receiving (300 kg/ha NPK 20-20-0). Results indicated that significant differences were observed among the three experimental sites representing contrasting soil types for soil chemical properties and maize productivity. Calcaric fluvisol exhibited the highest production potential, attaining a mean yield of 11,702.78 kg/ha. The impact of N supplementation on soil N levels and maize yield was found to be significant. The variant receiving urea supplementation (V1) achieved the highest median yield of 9560 kg/ha in comparison to the 7420 kg/ha obtained in the control. A strong positive correlation was observed between N index and yield across all soil types (ρ = 0.93 to 0.97, p < 0.001). Fertilization significantly influenced soil pH, CaCO₃ content, nitrogen index, phosphorus availability, and maize yield, whereas humus content remained relatively stable among treatments. These findings indicate that a split-fertilization regime combining NPK with urea provides a favorable balance between productivity and cost-effectiveness and maize output in the Transylvanian Plateau. Full article

We study the extreme behavior of six central Chile glacier mass balance series facing significant retreats and ice wastage due to climate variability and change. This has led to reduced meltwater availability in dry seasons, increasing competition for downstream water resources. Understanding glacier mass balances is crucial for predicting future water availability in scenarios with higher water demands. We used Extreme Value Theory tools to analyze the data and identify extreme events. The main objective of this study is to statistically analyze glacier mass losses in Chile, using mass balance data collected from both national and international sources. The results show high heterogeneity in the extreme behavior of glaciers, with some showing an approximately exponential tail (Guanaco Glacier), others exhibiting stability with slight tails (Echaurren Norte and Mocho Glaciers) and one (Amarillo Glacier) with a highly unstable structure. The other analyzed glaciers (Juncal Norte and Juncal Sur) have slight and potentially limited tails. These results confirm the high importance of studying glaciers in the Andes in order to better understand their responses to climate change, an important and relevant aspect for the future management of glacier melt water resources. Full article

The Tietê River, heavily polluted by the largest Brazilian city (São Paulo), has significant ecological and socioeconomic importance. The effects of water-quality degradation on zooplankton diversity (taxonomic and functional) and limnological variables were evaluated through a comparison of the Tietê River’s main channel, one of its marginal lagoons and a low-impacted tributary. Samplings covered both a rainy and a dry season. Environmental conditions are distinctive, with the main river and lagoon classified as hypereutrophic and the tributary as oligo-mesotrophic. The zooplankton, an essential community for aquatic ecosystem functioning, also exhibited remarkable spatial variation. Richness varied between six (lagoon/dry) and 43 (tributary/rainy). There was a much higher abundance in the lagoon (mean = 6.5 × 10⁵), followed by the Tietê River (mean = 4.0 × 10⁴) and tributary (mean = 2.5 × 10³), and a higher taxonomic diversity (Shannon mean = 2.98) and functional richness (mean = 0.66) in the tributary, contrasting with the intermediate values in the Tietê River (means of 1.7 and 0.31, respectively) and lower in the lagoon (1.49 and 0.01). Zooplankton from the Tietê River and the lagoon positively correlated with pH, total solids, chlorophyll and phosphorus. Negative pollution’s effects on the zooplankton community were intensified by the lagoon’s lentic hydrodynamics. The low-impacted tributary seems to act as a refuge for the regional zooplankton biodiversity, taxonomically and functionally, which is restricted to highly tolerant species in the main river. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

Accurate estimation of winter wheat aboveground biomass (AGB) is essential for crop growth monitoring and precision agricultural management. To reduce the effects of canopy structural complexity and spectral saturation on AGB estimation, this study evaluated winter wheat grown under different compost substitution ratios and planting densities. Based on unmanned aerial vehicle (UAV) multispectral and RGB imagery acquired over two growing seasons at four key growth stages, spectral vegetation indices, colour vegetation indices, and canopy structural features were extracted and integrated. Recursive feature elimination, Elastic Net, and support vector regression were used to construct stage-specific AGB estimation models. The optimal feature strategy varied among growth stages, indicating that AGB estimation requires stage-specific feature selection rather than a single fixed feature combination. The proposed framework achieved validation R² values of 0.872, 0.898, 0.867, and 0.895 at the jointing, booting, flowering, and grain-filling stages, respectively, and the corresponding RRMSE values were 12.5%, 12.1%, 14.3%, and 12.0%, respectively. Additional comparisons with PLSR, RF, and XGBoost based on the stage-specific optimal feature sets further confirmed the competitive performance of SVR under the present small-sample and multi-source feature conditions. Model improvement was more evident at the flowering and grain-filling stages. At these stages, the integration of selected spectral, colour, and structural features better represented canopy closure, spike-layer formation, and late-season biomass variation. Under the treatment combining 20% compost substitution with a planting density of 4.5 million plants ha⁻¹, winter wheat maintained relatively high AGB levels across growth stages. The novelty of this study lies in demonstrating that the effectiveness of multi-source UAV feature fusion for winter wheat AGB estimation is growth-stage dependent and is enhanced when coupled with feature selection. These findings provide a methodological reference for multi-temporal AGB monitoring and precision cultivation management under similar field conditions. Full article

Essential oils (EOs) from wild Calamintha nepeta (Portugal) populations collected in Portugal (Évora) were investigated in order to evaluate the impact of Mediterranean seasonal conditions on their phytochemical composition and biological activity. Essential oil GC-FID and GC-MS analyses revealed distinct seasonal chemotypes, with spring samples dominated by isopulegone/pulegone, whereas autumn samples contained higher proportions of isomenthone and menthol. Antioxidant activity was assessed through lipid peroxidation inhibition, DPPH radical scavenging and ferric reducing power assays, while antibacterial activity was evaluated against multidrug-resistant (MDR) clinical isolates. Seasonal differences were reflected in both EO chemical composition and bioactivity. Autumn samples displayed greater antioxidant potential, with Y1A showing the highest inhibition of lipid peroxidation (IC₅₀ = 0.85 mg/mL) and Y2A exhibiting the highest ferric reducing power. Conversely, spring samples were more active against MDR bacteria. Among them, Y1S showed the broadest antimicrobial spectrum, with MIC values ranging from 465 to 1767 μg/mL. The unusually wet spring season coincided with marked isopulegone accumulation (≈50%), while warmer autumn conditions favoured higher levels of isomenthone and menthol in the EOs. These findings highlight the importance of seasonal environmental conditions in determining the phytochemical profile and bioactive potential of C. nepeta EOs, providing valuable insights for their standardisation and valorisation in pharmaceutical, food and conservation-related applications. Full article