Search Results (687)

Brown algal forests (Cystoseira sensu lato) are key habitat-forming components of temperate rocky coasts but have experienced widespread decline across the Mediterranean Sea. This study investigates the current distribution and potential future shifts in brown algal forests across the Adriatic Sea under ongoing climate change. We combined non-destructive field-based mapping along the Slovenian coastline with remote-sensing products and spatial environmental predictors to model basin-wide habitat suitability. A multiscale geographically weighted regression (MGWR) framework was applied to account for spatial non-stationarity and to explicitly capture the fact that environmental drivers of habitat suitability operate at different spatial scales—an assumption that global models such as GAM or standard GWR cannot adequately address. Habitat suitability maps were generated for present-day conditions and projected under mid- and late-century climate scenarios. The results reveal pronounced latitudinal gradients, identify areas of ongoing canopy decline in the northern Adriatic, and highlight parts of the southern Adriatic as potential climate refugia. Overall, the study demonstrates a likely north–south contraction of suitable habitat for brown algal forests and underscores the value of multiscale spatial modelling for informing marine spatial planning, conservation prioritization, and climate-adaptive restoration under European policy frameworks. Full article

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions. Full article

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods. Full article

Sun glint is commonly observed as interference in the imaging process of ocean color satellite sensors, making the extraction of water color information in sun glint-affected areas challenging and often leading to significant data gaps. The remote sensing baseline indices, calculated based on Rayleigh-corrected reflectance ($R_{\mathrm{rc}}$), are recognized as effective in reflecting water color variability in sun glint-affected regions. However, the accurate extraction of the $R_{\mathrm{rc}}$ baseline indices requires sun glint correction. The determination of sun glint correction coefficients for different bands lacks a clear methodology, and the currently available correction coefficients are not applicable to different sea regions. Therefore, this study focuses on the South China Sea, where VIIRS imagery is significantly affected by sun glint. Based on paired datasets comprising sun glint-affected and -unaffected images acquired over the same region on adjacent dates, sun glint correction coefficients for each spectral band were derived by maximizing the cosine similarity of histograms constructed from three baseline indices: SS486 (Spectral Shape index at 486 nm), CI551 (Color Index at 551 nm), and SS671 (Spectral Shape index at 671 nm). To further evaluate the effectiveness of the proposed correction, chlorophyll-a concentrations were retrieved using a Random Forest regression model trained with baseline indices derived from sun glint-free $R_{\mathrm{rc}}$ data and subsequently applied to baseline indices after sun glint correction. Comparative analyses of both baseline index extraction and chlorophyll-a retrieval demonstrate that the proposed optimal-value and mean-value correction approaches effectively mitigate sun glint effects. The mean sun glint correction coefficients $\alpha$(443), $\alpha$(486), $\alpha$(551), $\alpha$(671) and $\alpha$(745) were determined to be 0.75, 0.83, 0.89, 0.95 and 0.94, respectively. These coefficients can be applied as sun glint correction coefficients for the VIIRS $R_{\mathrm{rc}}$ data in the South China Sea region. Furthermore, the proposed method for determining sun glint correction coefficients offers a transferable framework that can be extended to other sea areas. Full article

Although marine forest restoration projects have been widely implemented along the Korean coast, most evaluations have relied on simple structural indicators such as seaweed coverage or biomass, leaving functional responses of benthic macrofaunal communities largely unexplored. This study examined the effects of marine forest restoration on the functional structure of macrozoobenthic communities at development sites along Korea’s eastern coast in 2021 and 2024. Seaweed biomass increased significantly in 2024 compared to that in 2021, and this increase in seaweed biomass showed a clear positive correlation with increases in species number, density, and biomass of macrozoobenthos. Changes in feeding types of macrozoobenthic communities were remarkable, with grazer density increasing most sharply, followed by carnivores, omnivores, and suspension feeders. Red algal biomass was also positively correlated with suspension feeders and grazers, suggesting that seaweed mediated habitat and secondary food-web structures beyond providing simple food resources. These results indicate that seaweed habitat restoration plays an important role in recovering the functional diversity and feeding guild composition of macrozoobenthic communities and demonstrates the potential of using both species and functional diversity indicators to evaluate the effectiveness of marine forest restoration projects in Korea. Full article

The SE Baltic area, the former Eastern Prussia, is renowned for complex natural history. Over the past millions of years, the area experienced major geological events and geomorphic landscape transformations, resulting in the present relief configuration. Past climates and environments gave rise to the specific life-forms that proliferated in the Paleozoic and Mesozoic–Early Cenozoic shallow sea/lacustrine basins, and the Late Cenozoic riverine and continental settings. During the Paleogene, forested sub-tropical lands and deltaic settings of coastal sea lagoons gave rise to the famed amber formations (Blue Ground) hosting inclusions of resin-sealed insect and other small invertebrates that offer an unprecedented look into the 35–34 million-year habitats. Ferruginous sandstones, formed in shallow waters incorporating remains of thermophilous fauna—bivalves and gastropods, bryozoans, and sea urchins, among others—lie above the amber-bearing deposits. Oligocene–Miocene continental (riverine, lacustrine, and palustrine) conditions relate to the “Brown Coal Formation”, embedding a variety of fossil plants. Finally, the Quaternary Period brought dramatic geo-environmental shifts, with cyclic interstadial sea transgressions and massive glacial erosion events delivering fossiliferous erratics with an array of primitive Paleozoic and later Mesozoic life-forms. Overall, the extraordinary paleontology of the SE Baltic area adds, within its geological context, to the European geoheritage and the world natural heritage. Full article

This study is one of the first comprehensive assessments of soil carbon dynamics on the Black Sea coast of Russia, focusing on the role of soils in the terrestrial carbon cycle and the greenhouse gas balance of sub-Mediterranean ecosystems. Our integrated approach combined soil classification with the analysis of the distribution of organic and inorganic carbon, as well as the measurement of microbial biomass and respiration. Soil respiration components, including substrate-induced respiration (SIR) and basal respiration (BR), as well as greenhouse gas (carbon dioxide (CO₂) and methane (CH₄)) dynamics, were evaluated using a combination of laboratory and field measurements. Our results revealed significant differences between natural Rendzic Leptosols and terraced Skeletic Rendzic Leptosols (Technic and Transportic types). The latter contained higher organic carbon stocks (up to 25 kg m⁻²) associated with buried humus horizons, whereas the former were dominated by inorganic carbon accumulation. Microbial biomass carbon (MBC) ranged from 113 to 1119 µg C g⁻¹ of soil and decreased with depth. Basal respiration averaged 0.39 ± 0.30 µg C–CO₂ g⁻¹ h⁻¹. CO₂ emissions were strongly correlated with soil temperature (r = 0.65, p < 0.05) and negatively correlated with soil moisture, reflecting the predominant influence of abiotic factors. Seasonal chamber observations confirmed that these soils consistently function as CH₄ sinks, with negative CH₄ fluxes recorded across all seasons. Thus, Rendzic Leptosols on the Black Sea coast serve as significant CO₂ sources and stable CH₄ sinks simultaneously, and anthropogenic terracing enhances their potential for organic carbon sequestration. These findings refine our understanding of the carbon balance in sub-Mediterranean forest soils and highlight their dual role in greenhouse gas dynamics under changing climate conditions. Full article

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions. Full article

Addressing the limitations of traditional acoustic turbidity inversion models in complex marine environments—specifically their reliance on empirical parameters and lack of vertical resolution—this study presents a novel CNN-ResNet-RF hybrid model based on the simultaneous ADCP and turbidity observations in the Chengshantou sea area. Unlike conventional approaches, the proposed framework integrates deep spatio-temporal features automatically extracted by a ResNet-enhanced CNN, utilizing a Random Forest (RF) regressor for final prediction, thereby avoiding the limitations of artificial feature engineering. To ensure rigorous evaluation and mitigate stochastic bias, the model was validated using a 5-fold cross-validation strategy with dynamic Z-score normalization. Experimental results demonstrate that the proposed model significantly outperforms benchmark methods (CNN, RF, and CNN-RF), achieving an average R² of 0.782, an MAE of 4.454, and a MAPE of 15.42% on the test sets. This study confirms that the hybrid framework successfully combines the feature extraction power of deep learning with the robustness of ensemble learning, providing a robust and high-precision tool for the vertical structural analysis of ocean turbidity. Full article

The previously published liverwort checklist of Laos, one of the least-studied countries in Asia, was titled “Listing the Unknown”, based on the fact that only 66 species are known for such a landscape-diverse country. Our collection revealed 39 genera and 76 species, 62 of which are newly recorded species to the country, bringing the total number of known species to 128. Among the reported genera, there are 22 liverwort genera new to Laos, all of which could have been expected in this area. Although new data expands the species list, the total number of species recorded remains inadequately small. The presented studies are based primarily on collections at lower elevations (below 500 m above sea level), in strongly modified secondary forest conditions, and are of interest specifically as an example of the liverwort flora of heavily modified, anthropogenically disturbed habitats of rather dry tropical forest communities. The provided checklist includes data on the ecological conditions of the collected species and their altitudinal range. Further research on the liverwort flora of Laos should be conducted in the upper altitudinal zones of the north and the east of the country. Full article

This review analyzes current research on short-term culture of sea urchin from barrens through formulated feed, addressing the need for sustainable aquaculture practices and ecological restoration of kelp forests. We compare the results of multiple studies to identify the optimal feed composition to induce gonad growth and coloration. Our analysis suggests that macroalgae are the best feed ingredients to improve gonad growth and coloration; however, environmental and economic challenges persist in expanding sea urchin production with these types of ingredients. Plant-based protein sources like soy have emerged as a potential cost-effective alternative to fish products; nevertheless, the presence of antinutritional factors in soy products limits their inclusion in formulated feed. Regarding the composition and amount of lipids, we found that they are critical macronutrients in gonad development. The review also explores the potential of sea urchin aquaculture in mitigating urchin barrens and restoring kelp forests, highlighting the interplay between ecological and economic factors. We identify key knowledge gaps and propose future research directions, including large-scale economic viability assessments, novel feed additives, and integrated multitrophic aquaculture systems. These findings have significant implications for developing sustainable and economically viable sea urchin aquaculture, potentially transforming urchin barrens into productive ecosystems while meeting market demand for roe. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Environmental education, implemented at both formal and informal levels, plays a significant role in the transformation process towards a Circular Economy (CE). In the Baltic Sea Region (BRS), the significant role of the forestry sector is worth noting, as it contributes to strengthening the CE agenda through the sustainable and circular management of wood processing waste. However, currently, environmental education on the potential uses of this waste, for the general public (including youth), students, and professionals, is quite limited. Therefore, this paper presents a conceptual approach to developing an education roadmap. The scope of work includes identifying the education gap in the forestry sector using a questionnaire survey among residents of the Baltic Sea Region, and then developing a concept for an education roadmap consistent with the CE assumptions. The presented concept of roadmap is a comprehensive document that analyses the educational needs, challenges, and opportunities related to the sustainable use of forest biomass in a given region. Strategic assumptions and educational priorities were identified and implemented in this document. Our findings contribute to aligning forestry education with broader environmental and economic goals in the Baltic Sea Region and beyond. This study supports the achievement of Sustainable Development Goals 4 (Quality Education), 12 (Responsible Consumption and Production), and 15 (Life on Land) by providing practical insights for advancing circular economy education in natural resource management. Full article

Geographical origin determination for seafood products is a fundamental aspect due to its implications for fraud prevention, ensuring food safety, and promoting resource sustainable management. In this research, different machine learning (ML) models based on random forests, support vector machines, and artificial neural networks were fed with trace element fingerprinting (TEF) and stable isotope ratio analysis (SIRA) to determine the origin of mussels that have been farmed in eight regions and ten locations around the world (areas of the European Atlantic coast, the Mediterranean Sea, and the Pacific coast of Chile). Fourteen trace elements in shells and carbon and nitrogen isotope ratios of mussel tissue were used singly, in combination, or reduced to develop the different approach models. All the selected models present high prediction accuracies for the independent variables (except for SIRA models), for their combination, or for their optimisation, highlighting the artificial neural network and random forest models that presented a 100% accuracy for all cases using a combination of variables selected based on a random forest model TEF to predict region and location, respectively. This fact confirms that ML models are suitable approximation techniques to determine the region and location of Mediterranean mussel origin, with key applications in food safety and global sustainability. Full article

Coastal flooding driven by sea-level rise and land subsidence poses severe risks to low-lying communities. This study evaluates the causes and impacts of coastal sinking in Sayung, Demak, Central Java, using multi-temporal Landsat imagery (1977, 2024), tidal gauge data, and GPS measurements. A set of spectral indices—Normalized Difference Vegetation Index (NDVI), Weighted Modified Normalized Difference Water Index (WMNDWI), Land Surface Water Index (LSWI), and Normalized Difference Built-up Index (NDBI)—were calculated and integrated as input features for a Random Forest machine learning model to detect and classify environmental changes. Results indicated an average land subsidence rate of approximately 6 cm/year ± 0.8 cm/year, validated against InSAR-based measurements, and a classification accuracy of 91% (RMSE of 0.8 cm/year). A substantial decline in vegetation indices was observed, reflecting the conversion of agricultural land into built-up areas and water bodies. Extensive flooding and shoreline retreat were documented, with high-risk zones concentrated along densely developed coastlines. These findings highlight the urgent need for integrated management strategies, including stricter groundwater regulation, continuous remote-sensing-based monitoring, and large-scale mangrove restoration, to safeguard ecological functions and enhance the socio-economic resilience of coastal communities in the face of accelerating climate change impacts. Full article