Search Results (4,613)

In 2024 and 2025, researchers investigated the breeding ecology of the Twite Linaria flavirostris in riparian shrubland habitats at an elevation of 3400 m in the northeastern Qinghai–Tibet Plateau. This species lays eggs from late June to mid-July, capitalizing on the region’s brief warm season. Nests are typically open-cup structures built in Hippophae spp. shrubs. The population predominantly exhibits monogamous mating, with a mean clutch size of 4.7 ± 0.49 (3~5). Incubation is performed solely by the female and lasts 11.52 ± 1.65 days. Both parents provision the nestlings, and the nestling period lasts 12.43 ± 2.39 days. Morphological measurements of nestling body mass and external organs all fit well to the Logistic growth curve equation. By fledging, tarsus length and bill length reach over 90% of adult values, conferring substantial terrestrial mobility. However, flight-related feathers, primaries and rectrices, remain markedly underdeveloped compared to adults, resulting in extremely poor flight capability; further post-fledging development is thus required. Based on reproductive outcomes from this single breeding season, a total of 121 eggs were laid, of which 81 successfully hatched, and ultimately 79 fledglings survived to leave the nest. The overall hatching success was 66.94%, fledging success (among hatchlings) was 97.53%, and overall offspring survival (from eggs to fledglings) was 65.29%. The apparent nesting success rate was 76.0%, based on a total of 50 nests monitored over two years. Daily nest survival rates were estimated using Mayfield’s method and program MARK, resulting in nest success probabilities of 0.587 and 0.219, respectively. Comparing populations across different geographic regions, the results indicate that Twites breeding in environments with higher levels of environmental stress produce smaller clutch sizes and larger eggs, and exhibit a prolonged nestling period. This life-history strategy likely represents an evolutionary adaptation to spatially variable environmental conditions. Full article

The circulation of the North Atlantic Ocean plays a vital role in the Earth’s climate system. Numerous studies, primarily through computer simulations, have examined the stability of the Atlantic Meridional Overturning Circulation (AMOC) in a warming climate. Some of these studies predict a potential collapse of the AMOC in the foreseeable future, which would require a significant influx of freshwater into the subpolar North Atlantic (NA) and Nordic Seas. Paleoreconstructions of NA circulation indicate a major shift in the position of the subpolar cold front, which either precedes or coincides with substantial changes in AMOC dynamics. These changes in the front position imply a significant alteration in circulation patterns, beginning with the noticeable restructuring of the subtropical and subpolar gyres. This would lead to modifications in the Gulf Stream system and the North Atlantic Current (NAC), affecting the thermohaline fields and the position and strength of these two current systems. Although some models predict a significant slowdown or even collapse of the AMOC, recent observational studies have offered a more cautious perspective. For instance, the Gulf Stream system exhibits high resilience to perturbations caused by ongoing sea surface warming. In this study, we analyzed the decadal variability of temperature and salinity from in situ observations, along with upper-ocean currents in the subpolar NA (SPNA). We found that the thermohaline pattern of the upper ocean layers in the SPNA and Nordic Seas has remained resilient for over 70 years. The deceleration of the AMOC is evident but relatively modest, with average velocities in the upper layers decreasing by less than 10–15% over 30 years. This deceleration was also inconsistent throughout the NAC region. Furthermore, the subpolar front migration over 70 years, as manifested in isotherm spatial variability, reached a maximum of 3° of latitude, with spatial variability of the yearly 10 °C isotherms being lower. Overall, the conclusion regarding the resilience of the NAC aligns well with that of the Gulf Stream, with no substantial changes in the position or intensity of the subpolar gyre. We conclude that while the AMOC is susceptible to some deceleration due to ongoing surface warming and/or high-latitude freshening, it may also be sufficiently resilient to withstand these changes. Although it cannot be entirely ruled out that the AMOC may reach its tipping point within this century, an analysis of data on decadal variability in the upper arm of the AMOC suggests that such a collapse is unlikely to occur. Full article

The Yellow Sea is an important offshore area in China, and the accurate prediction of its seawater temperature is of great significance for marine environmental monitoring and climate adaptation management. However, existing research on predicting the three-dimensional (3D) temperature field in the Yellow Sea is scarce and insufficiently accurate. This study proposes a TCN-U-Net++ fusion model to reconstruct the Yellow Sea temperature field using remote sensing satellite data and SODA reanalysis data, while considering the influence of a series of factors, including wind (USSW and VSSW), absolute bathymetric data (BAT), sea surface height anomaly (SSHA), latitude (LAT), longitude (LON), solar radiation (SR), surface runoff (SRO), and precipitation (P). The results show that the model can accurately capture the temporal and spatial distribution characteristics of the temperature field in the Yellow Sea. The results indicate that the deviations from SODA are generally within 2 °C, with errors being approximately 45% lower than those of other models, while the prediction errors relative to Argo and voyage observations are mostly within 1 °C, further demonstrating the accuracy and robustness of the proposed model. In addition, the predictions of the Yellow Sea Cold Water Mass (CWM) are highly consistent with SODA in terms of their evolution and key characteristic parameters. Specifically, the maximum deviation in core temperature is only 0.3 °C, and the difference in its spatial extent is less than 1%. The results demonstrate that TCN-U-Net++ effectively enhances the accuracy of 3D sea temperature prediction in the Yellow Sea, providing technical support for temperature monitoring, ecological early warning, and climate change research. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

Anthropogenic activities and environmental changes have exerted an increasingly high impact on the habitats of wild animals, especially endangered species. Researchers have paid attention to the effects of future climate change on wildlife habitats. However, the impact of climate change on the suitable habitats of Tragopan birds has rarely been reported. Here, we used the Maxent model to assess the influence of climate change on the geographical distribution of five Tragopan species. The results showed that the SSP585 scenario projected relatively favorable conditions, with the total area of suitable habitats expected to show an overall increasing trend over time. Centroid analysis revealed that the centroid gradually shifts toward lower latitudes and elevations due to climate warming. Environmental factor analysis showed that human-induced factors (particularly land use) are the main determinants affecting the habitat suitability of Tragopan birds. Notably, a comparison between dispersal velocity and biological velocity showed that despite the predicted gradual expansion of habitat area, Tragopan birds may be difficult to expand into the newly suitable habitat regions. We further emphasize that establishing ecological corridors and setting up new protected areas will have a more significant impact on conserving the Tragopan birds. Full article

Thailand preserves one of the most extensive records of Mesozoic vertebrate tracks in Tropical Asia, yet these ichnological data have never been comprehensively synthesized. This review compiles and reassesses all known Triassic to Cretaceous vertebrate tracksites in Thailand to clarify their stratigraphic distribution, taxonomic diversity, and palaeobiogeographical significance. Published records, new field observations, and updated stratigraphic correlations are integrated to evaluate trackmaker attributions and temporal patterns. The Thai record documents diverse assemblages including chirotheriids, early theropods, sauropodomorphs, ornithopods, sauropods, and crocodilians. Late Triassic–Early Jurassic assemblages capture a major faunal transition, revealing the co-occurrence of non-dinosaurian archosaurs and some of the earliest dinosaurs in the region, whereas Lower Cretaceous sites are dominated by theropods, sauropods and diverse ornithopods. Comparison with other Asian ichnofaunas indicates faunal continuity across eastern Asia and supports early dinosaur dispersal into equatorial low latitudes. This synthesis also evaluates site conservation, highlighting the vulnerability of several Triassic localities and a positive trend of community-led discoveries since 2009, underscoring the need for proactive management and standardized digital documentation. Overall, the Thai ichnological succession represents the most complete Mesozoic track record presently known from Tropical Asia and provides key insights into vertebrate evolution, palaeoecology, and regional biogeography. Full article

Terrestrial ecosystems play a critical role in regulating atmospheric CO₂ through land–atmosphere carbon exchange. While Net Ecosystem Exchange (NEE) serves as a key integrative metric for carbon dynamics, its robust global estimation remains challenging due to profound environmental heterogeneity and nonlinear ecosystem responses. In this study, we propose a dual-track experimental framework to invert annual global terrestrial NEE at a 0.1° spatial resolution for 2000–2024. Initially, a long-term historical baseline inversion (2000–2024) was developed using explicit multi-source environmental predictors. Subsequently, to overcome the representational limitations of conventional spectral indices over complex terrains, we integrated high-dimensional remote-sensing embeddings from the AlphaEarth framework for the 2017–2024 overlapping period. This approach was designed to explicitly quantify the added value of these advanced features. Our results demonstrate that embedding features substantially enhance inversion performance, reducing prediction errors and improving spatial coherence. Adopting the standard meteorological sign convention, global terrestrial NEE remained consistently negative. Based on the 2000–2024 baseline inversion, our predicted global NEE fluctuated between −3.50 and −4.38 Pg C yr⁻¹. To validate these long-term estimates, we systematically cross-validated our results against an independent, recently published multi-network fusion dataset, which reported a comparable range of −3.11 to −3.75 Pg C yr⁻¹. This comparison demonstrates consistent interannual dynamics and corroborates the magnitude of the global terrestrial carbon sink. Spatial patterns exhibit a stable latitudinal structure, with stronger net carbon uptake in low latitudes. Interannual variability is expressed mainly as magnitude fluctuations rather than systematic spatial reorganization. Overall, this study highlights that high-dimensional Earth observation embeddings provide significant, measurable information gains for global NEE inversion without introducing new process-based assumptions, thereby offering a robust and internally consistent basis for evaluating long-term carbon dynamics. Full article

Urban ecological vulnerability has become an important perspective for understanding ecosystem stability under environmental change. However, its spatiotemporal dynamics and driving mechanisms remain insufficiently understood in high-latitude grassland cities. This study focuses on the central urban area of Hailar and examines how ecological vulnerability evolves and what factors shape its spatial differentiation. Using the sensitivity–resilience–pressure (SRP) framework, a multidimensional evaluation system was constructed based on statistical yearbooks and GIS-based spatial data. Ecological vulnerability was assessed on a 1 km grid from 2010 to 2020, and its evolution was analyzed in three stages. The spatial pattern remains relatively stable but shows increasing differentiation over time. High-vulnerability areas are persistently concentrated in built-up regions, while low-vulnerability areas are mainly located in surrounding forest and grassland ecosystems with higher ecological resilience. Over time, vulnerability gradually shifts outward from the urban core, with clear intensification along the urban fringe. The results indicate that ecological vulnerability is driven by the interaction of sensitivity, resilience, and pressure, while urban expansion plays a key role in intensifying ecological stress and reshaping spatial patterns. The study provides a framework for understanding ecological vulnerability dynamics in high-latitude resource-based grassland cities and supports zoning-based ecological management and land-use optimization. Full article

Dry soil spectral reflectance provides a stable baseline for characterizing soil optical properties and supporting the retrieval of soil attributes from remote sensing. However, despite the large number of studies on soil spectral reflectance, most existing research primarily focuses on empirical relationships between spectra and soil properties. The representation and prediction of dry soil reflectance as a baseline condition, particularly under the influence of environmental factors, remain insufficiently explored, and the generalizability of existing models still needs improvement. Therefore, this study collects 700 dry soil samples with laboratory-measured spectral reflectance from Northeast China and quantitatively analyzes the contribution of environmental covariates (soil properties, parent material, and geographical location) using the SHAP method. Then, an environmental and edaphic-factor-driven smooth dry soil reflectance model (EEDSR) model covering 400–2500 nm is developed based on gradient boosting regression (GBR), and its accuracy is evaluated using global ISRIC soil datasets. Our results indicate the following: (1) the reflectance of dry soil is closely related to the soil properties in the VIS to SWIR range. The reflectance of dry soil of 400–2500 nm is positively correlated with clay percentage, longitude, and parent material but negatively correlated with latitude, sand percentage and silt percentage. And its correlation with other variables (such as soil organic matter, pH, and EC) varies with wavelength. (2) The EEDSR model exhibited high predictive accuracy across the 400–2500 nm spectral range (R² = 0.93, RMSE = 0.018). Additionally, incorporating parent material (PM) and geographical factors into the predictor set enhanced the accuracy of dry soil reflectance prediction by 13.4%. (3) The spatial consistency between the predicted soil reflectance in Northeast China and the satellite observations indicates that the EEDSR model has good performance in predicting soil reflectance, as the bias of reflectance gradually increasing from west to east is consistent with the precipitation distribution in Northeast China. (4) The generalization ability of the EEDSR model was confirmed by global ISRIC datasets (R = 0.94), outperforming the deep learning-based Soil Optical Generative Model (SOGM) (R = 0.27). Overall, this study presents an efficient and interpretable framework for modeling dry soil spectral reflectance, providing a robust reference for soil reflectance prediction and remote sensing-based soil property retrieval. Full article

Commercial production of sweet cherries is possible up to approximately 60° N latitude in Norway and is among the most economically important fruit crops in the country. The harvest is late, but yields are very high, and the fruit is intended solely for the fresh market. The objective of this study was to assess whether sweet cherry can be grown in pots and to determine fruit quality (sugar, acid, polyphenol, and mineral content) of three sweet cherry cultivars (‘Van’, ‘Lapins’, and ‘Regina’) grown in high tunnels with varying levels of fertigation (F) and the application of slow-release (SR) fertilisers. Trees were planted in 35 L plastic bags, trained as spindle trees, with a spacing of 1 × 2.5 m (4000 trees/ha). The tunnel was covered with polythene from flowering until harvest. Fruit produced in pots had low levels of sugars and acids and high levels of phenolic acids and flavonoids, while the mineral content depended on treatment and cultivar. The main sugar components (glucose and fructose), the sweetness index, phenolic acids (chlorogenic acid and ferulic acid), flavanols (catechin, rutin, quercetin, and hyperoside), and minerals (P, K, Mg, Ca, and Na) were much higher in the F treatment. SR treatments were more effective in increasing the content of acids (shikimic, malic, and quinic) and total phenolic content (TPC). Radical scavenging activity (RSA) and total sugars showed no statistically significant differences between the treatments studied. ‘Lapins’ fruit obtained from the fertigation regimes (when Kristalon brown + Calcinit + Magnesium-sulphate were added from mid-April to 1 September and plain water for the rest of the season, up to an electric conductivity (EC) of 0.5 and 1.0) contained the highest levels of minerals (P, K, Mg, Ca). The ‘Van’ cultivar from F treatments, especially VF2 (when Kristalon brown + Calcinit + Magnesium-sulphate were added from mid-April to 1 September and plain water for the rest of the season, with EC 1.0) and VF3 (when Kristalon brown is added in July, Kristalon brown + Calcinit + Magnesium-sulphate in August, and plain water for the rest of the season) had the highest sweetness index, glucose, fructose, chlorogenic acid, ferulic acid, and hyperoside in sweet cherry fruit. ‘Regina’ under the RSR1 (50 g Multicote and 30 g chalk lime per tree) and RSR2 regimes (100 g Multicote and 30 g chalk lime per tree) produced fruit with the highest acid components, RSA and TPC. This suggests that sweet cherry trees can be grown in pots under high tunnels, but nutrition should be adjusted for each cultivar according to its physiological responses to specific microclimate conditions. Full article

Differing from previous work on ionospheric models only using a relative method, in this paper, stereoscopic ionospheric models are innovatively constructed utilizing both relative and absolute automatic plasma perturbations. Firstly, ionospheric perturbations are globally searched from electron density data measured for 10 years by Swarm-A/C satellites via automatic detection software. In total, 621,999 Swarm-A perturbations and 630,668 Swarm-C ones are obtained, respectively. Then, the variation for each perturbation is calculated in two ways: via the relative method and absolute method. To check possible discrepancy between ionospheric models under these two different calculations, seasonal ionospheric models have been globally established using relative and absolute perturbations for both satellites. The results show that both kinds of models for each satellite can comprehensively reveal the main ionospheric structures, like EIA, WSA/MSNA, the mid-latitude trough and the auroral anomaly zone. Relatively, the EIA always shows its significance in equinox under calculation methods due to strong ionospheric irregularities caused by seasonal variation, but it is more obvious under the absolute method than relative one because of its higher background density. Comparatively, the auroral anomaly zone is predominantly filled with relatively large perturbations and is particularly conspicuous, especially in winter, due to its low background density. By contrast, mid-latitude structures, such as WSA/MSNA and mid-latitude trough, are comparatively affected less under these dual methods. At the same time, the interhemispheric asymmetry of EIA phenomena, as well as latitudinal WN4/3, is also significantly distinguished by seasonal ionospheric models. The occurrence probabilities of perturbations as a function of various variation magnitudes are also examined and the results demonstrate that the percentages of all variation segments vary widely with seasonal changes but this uneven fluctuation is more pronounced in summer under relative calculation and in winter under absolute calculation. Small fluctuations with relative variation ΔVr < 10% or absolute ΔVa < 10⁴ m⁻³ always demonstrate significance in each group of seasonal perturbations while their percentage changes in different ways, decreasing in the order of summer, equinox and winter under the relative method and increasing under the absolute method. The measurements performed by Swarm-A/C demonstrate excellent consistency during the period considered. Full article

Near-infrared spectroscopy (NIRS) offers significant advantages for the rapid and non-destructive detection of nutrients in livestock manure slurry. However, conventional models based only on spectral features often show limited robustness under cross-seasonal and multi-farm conditions due to differences in farm source, treatment stage, and complex spatiotemporal background. To improve the accuracy and applicability of total nitrogen (TN) prediction in dairy farm manure slurry, this study used 747 samples collected from 36 large-scale dairy farms in Tianjin, China, covering 24 treatment stages and four seasons, together with sample-contextual information such as farm name, longitude, latitude, and season. Competitive adaptive reweighted sampling (CARS) was applied to select key wavelengths from near-infrared spectra. On this basis, a multi-branch gated fusion deep learning model, MultiScaleSE-GatedCNN, was developed to integrate spectral and sample-contextual information. The model combines multi-scale one-dimensional convolution for spectral feature extraction, separate encoding branches for numerical and categorical inputs, and a gated fusion unit for adaptive weighting of different information sources. Results showed that partial least squares regression remained a strong baseline under single-source spectral conditions, but the proposed deep learning fusion model achieved superior predictive performance after introducing sample-contextual information. Ablation experiments demonstrated that different combinations of sample-contextual information contributed differently to model performance, and the combination of spectra, farm name, longitude, and season yielded the best results. Under this optimal input combination, MultiScaleSE-GatedCNN achieved a test-set R² of 0.905, an RMSEP of 367.389, and an RPD of 3.242. These results demonstrate that integrating NIRS with sample-contextual information can effectively improve the accuracy and robustness of TN prediction in dairy farm manure slurry. Full article

Morocco is among the regions in the Mediterranean basin most exposed to the impacts of climate variability and change. This increasing exposure requires a detailed and rigorous analysis of regional atmospheric dynamics to better understand the mechanisms behind recent climate trends. This study aims to examine the variability of circulation weather types (CWTs) at a regional scale over the period 1980–2019, within a geographical area bounded by latitudes 20° to 40° N and longitudes 10° to 22.5° W. The analysis is based on data from the NCEP-DOE Reanalysis 2, including mean sea level pressure (MSLP) and geopotential height at 500 hPa (Z500), with a spatial resolution of 2.5° in both latitude and longitude. The adopted methodology identifies daily CWT using a principal component analysis (PCA) in S-mode with Varimax rotation (PCAV), followed by the evaluation of their monthly distributions and temporal trends. The analysis highlights a marked trend toward increased atmospheric configurations conducive to hot conditions during the dry season, associated with the intensification and northward shift in the Saharan thermal low. This dynamic is reinforced by the increased frequency of ridges or high geopotential heights at 500 hPa, which transport warm tropical air toward the region. Moreover, the study reveals a notable decrease in the frequency of upper-level troughs at 500 hPa during the wet season. These upper-level troughs play a crucial role in cyclogenesis and the delivery of precipitation. These findings indicate a shift toward a regional atmospheric dynamic unfavorable to Morocco’s hydric balance, characterized by more frequent and intense summer heat and worsening winter drought. Full article

Offshore industries depend solely on diesel-based power generation systems or mainland grids, which are expensive and carbon-intensive. The demand for renewable energy-based offshore DC microgrids (MGs) has significantly increased due to rising fuel prices, high costs of fuel transportation and storage, extreme operation and maintenance expenses, and associated carbon emissions. This research study optimises the size of an offshore DC MG that integrates wave, solar, energy storage, and diesel, utilising real-world data from a specific geographical location (latitude −33.525587 and longitude 114.772211), thereby accurately representing the availability of renewable energy sources. An algorithm is designed to optimise the utilisation of highly variable renewable sources via battery-based energy management, resulting in optimal energy dispatch. Utilising economic performance metrics, such as levelised cost of energy (LCoE) and net present value (NPV), this research aims to minimise the energy, operating, and greenhouse gas emission costs while maximising the economic feasibility of the system. A sensitivity analysis is performed to determine the impact of fuel prices, discount rates, and system lifespans on the feasibility of the system. The findings demonstrate that the proposed renewable-based offshore DC MG can substantially reduce fuel consumption (93%), operational expenses (77.56%), and carbon emissions (89.50%) compared with a diesel-only system for offshore platforms, while improving the sustainability and reliability of power supply for aquaculture and marine activities. In addition, the proposed renewable-energy-based offshore DC MG achieves a lower LCoE (0.5649 $/kWh) and a higher NPV (2.987 × ${10}^4$ $) than a conventional diesel-based power generation system for offshore industries. The results provide a decision-making framework for the design and implementation of renewable energy-based offshore DC MGs. Full article

This study explored the application of carbon stable isotopes for tracing the geographical origin of Monochamus alternatus, an insect vector responsible for spreading pine wilt disease. The primary vector of pine wilt disease, an aggressive disease caused by the pine wood nematode and affecting pine forests, is Monochamus alternatus. Samples of Monochamus alternatus were collected from 12 provinces across China, and their carbon isotope ratios (δ¹³C) were measured. By analyzing the correlation between these ratios and various environmental factors, including latitude, longitude, altitude, and bioclimatic conditions, it was found that precipitation seasonality and solar radiation were the most important factors influencing the carbon isotope ratio of Monochamus alternatus. The spatial distribution of Monochamus alternatus carbon isotopes in China was predicted using the co-Kriging interpolation method, incorporating these two environmental variables. The findings revealed a gradient in the carbon isotope ratio of Monochamus alternatus, which could help differentiate the species across various geographical regions in China. Full article