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25 pages, 3878 KiB  
Article
Metagenomic Characterization of Microbiome Taxa Associated with Coral Reef Communities in North Area of Tabuk Region, Saudia Arabia
by Madeha O. I. Ghobashy, Amenah S. Al-otaibi, Basmah M. Alharbi, Dikhnah Alshehri, Hanaa Ghabban, Doha A. Albalawi, Asma Massad Alenzi, Marfat Alatawy, Faud A. Alatawi, Abdelazeem M. Algammal, Rashid Mir and Yussri M. Mahrous
Life 2025, 15(3), 423; https://doi.org/10.3390/life15030423 - 7 Mar 2025
Viewed by 157
Abstract
The coral microbiome is highly related to the overall health and the survival and proliferation of coral reefs. The Red Sea’s unique physiochemical characteristics, such a significant north–south temperature and salinity gradient, make it a very intriguing research system. However, the Red Sea [...] Read more.
The coral microbiome is highly related to the overall health and the survival and proliferation of coral reefs. The Red Sea’s unique physiochemical characteristics, such a significant north–south temperature and salinity gradient, make it a very intriguing research system. However, the Red Sea is rather isolated, with a very diversified ecosystem rich in coral communities, and the makeup of the coral-associated microbiome remains little understood. Therefore, comprehending the makeup and dispersion of the endogenous microbiome associated with coral is crucial for understanding how the coral microbiome coexists and interacts, as well as its contribution to temperature tolerance and resistance against possible pathogens. Here, we investigate metagenomic sequencing targeting 16S rRNA using DNAs from the sediment samples to identify the coral microbiome and to understand the dynamics of microbial taxa and genes in the surface mucous layer (SML) microbiome of the coral communities in three distinct areas close to and far from coral communities in the Red Sea. These findings highlight the genomic array of the microbiome in three areas around and beneath the coral communities and revealed distinct bacterial communities in each group, where Pseudoalteromonas agarivorans (30%), Vibrio owensii (11%), and Pseudoalteromonas sp. Xi13 (10%) were the most predominant species in samples closer to coral (a coral-associated microbiome), with the domination of Pseudoalteromonas_agarivorans and Vibrio_owensii in Alshreah samples distant from coral, while Pseudoalteromonas_sp._Xi13 was more abundant in closer samples. Moreover, Proteobacteria such as Pseudoalteromonas, Pseudomonas and Cyanobacteria were the most prevalent phyla of the coral microbiome. Further, Saweehal showed the highest diversity far from corals (52.8%) and in Alshreah (7.35%) compared to Marwan (1.75%). The microbial community was less diversified in the samples from Alshreah Far (5.99%) and Marwan Far (1.75%), which had comparatively lower values for all indices. Also, Vibrio species were the most prevalent microorganisms in the coral mucus, and the prevalence of these bacteria is significantly higher than those found in the surrounding saltwater. These findings reveal that there is a notable difference in microbial diversity across the various settings and locales, revealing that geographic variables and coral closeness affect the diversity of microbial communities. There were significant differences in microbial community composition regarding the proximity to coral. In addition, there were strong positive correlations between genera Pseudoalteromonas and Vibrio in close-to-coral environments, suggesting that these bacteria may play a synergistic role in Immunizing coral, raising its tolerance towards environmental stress and overall coral health. Full article
(This article belongs to the Special Issue Microbial Diversity and Function in Aquatic Environments)
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Figure 1
<p>The relative abundance of the main bacterial phyla (<b>A</b>), orders (<b>B</b>), and classes (<b>C</b>) in soil samples taken from three different localities in the northern Red Sea—Alshreah, Saweehal, and Marwan—at different distances from coral reefs. Pseudomonadota, Actinomycetota, and Bacillota were significantly enriched at the phylum level (<b>A</b>), whereas Alteromonadales, Vibrionales, and Moraxellales were the most common bacterial orders (<b>B</b>). The three most prevalent groupings at class level (<b>C</b>) were Actinobacteria, Gammaproteobacteria, and Alphaproteobacteria. These differences demonstrate how environmental variables and coral closeness affect the spread of microorganisms in various settings.</p>
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<p>The relative abundance of the main bacterial phyla (<b>A</b>), orders (<b>B</b>), and classes (<b>C</b>) in soil samples taken from three different localities in the northern Red Sea—Alshreah, Saweehal, and Marwan—at different distances from coral reefs. Pseudomonadota, Actinomycetota, and Bacillota were significantly enriched at the phylum level (<b>A</b>), whereas Alteromonadales, Vibrionales, and Moraxellales were the most common bacterial orders (<b>B</b>). The three most prevalent groupings at class level (<b>C</b>) were Actinobacteria, Gammaproteobacteria, and Alphaproteobacteria. These differences demonstrate how environmental variables and coral closeness affect the spread of microorganisms in various settings.</p>
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<p>Relative abundance of dominant bacterial family (<b>A</b>), genus (<b>B</b>), and species (<b>C</b>) in soil samples from three different locations, close and far from corals, in the northern Red Sea—Alshreah, Saweehal, and Marwan. The predominant families were Pseudoalteromonadaceae, with <span class="html-italic">Vibrionaceae</span> and Moraxellaceae following closely (<b>A</b>). At the genus level, the most prevalent were <span class="html-italic">Pseudomonas</span>, <span class="html-italic">Vibrio</span>, and <span class="html-italic">Psychrobacter</span> (<b>B</b>). In terms of species, <span class="html-italic">Pseudoalteromonas agarivorans</span>, <span class="html-italic">Vibrio chagasii</span>, <span class="html-italic">Vibrio owensii</span>, and <span class="html-italic">Pseudoalteromonas</span> sp. Xi13 were the most abundant (<b>C</b>).</p>
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<p>Relative abundance of dominant bacterial family (<b>A</b>), genus (<b>B</b>), and species (<b>C</b>) in soil samples from three different locations, close and far from corals, in the northern Red Sea—Alshreah, Saweehal, and Marwan. The predominant families were Pseudoalteromonadaceae, with <span class="html-italic">Vibrionaceae</span> and Moraxellaceae following closely (<b>A</b>). At the genus level, the most prevalent were <span class="html-italic">Pseudomonas</span>, <span class="html-italic">Vibrio</span>, and <span class="html-italic">Psychrobacter</span> (<b>B</b>). In terms of species, <span class="html-italic">Pseudoalteromonas agarivorans</span>, <span class="html-italic">Vibrio chagasii</span>, <span class="html-italic">Vibrio owensii</span>, and <span class="html-italic">Pseudoalteromonas</span> sp. Xi13 were the most abundant (<b>C</b>).</p>
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<p>PCA of bacterial community composition. This figure depicts how samples cluster by bacterial community composition using PCA. The samples are color-coded according to their distance from coral (far-from-coral vs. close-to-coral) and grouped according to location (Alshreah, Saweehal, and Marwan).</p>
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<p>Heatmap of bacterial abundance across samples. <a href="#life-15-00423-f002" class="html-fig">Figure 2</a> shows the top bacterial species’ relative abundance across all samples, through a 2D hierarchical clustering heatmap. In the heatmap, rows represen different bacterial species, whereas the columns correspond to the samples grouped together based on their proximity to the coral and location.</p>
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<p>Bacterial Communities in Coral Proximity and Distances.</p>
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13 pages, 2203 KiB  
Article
The Integration of a Medium-Resolution Underwater Radioactivity System in the COSYNA Observing System at Helgoland Island, Germany
by Christos Tsabaris, Stylianos Alexakis, Miriam Lienkämper, Max Schwanitz, Markus Brand, Manolis Ntoumas, Dionisis L. Patiris, Effrosyni G. Androulakaki and Philipp Fischer
J. Mar. Sci. Eng. 2025, 13(3), 516; https://doi.org/10.3390/jmse13030516 - 6 Mar 2025
Viewed by 176
Abstract
The continuous monitoring of radioactivity in a cabled subsea network in the North Sea Observatory was performed to test the performance of a medium-resolution underwater spectrometer, as well as to identify and to assess potential anthropogenic and/or natural hazards. The effectiveness of continuous [...] Read more.
The continuous monitoring of radioactivity in a cabled subsea network in the North Sea Observatory was performed to test the performance of a medium-resolution underwater spectrometer, as well as to identify and to assess potential anthropogenic and/or natural hazards. The effectiveness of continuous monitoring was tested together with the operability of the underwater sensor, and quantification methods were optimized to identify the type of radioactivity as well as the activity concentration of radionuclides in the seawater. In the frame of the RADCONNECT project, a medium-resolution underwater radioactivity system named GeoMAREA was integrated into an existing cabled ocean observatory placed on Helgoland Island (COSYNA network). The system could be operated via an online mode controlled by the operational centre (AWI), as well as remotely by the end-user (HCMR). The system provided gamma-ray spectra and activity concentrations of key radionuclides that were enriched in seawater during the monitoring period. As concerns the quantification method of natural radioactivity, the average activity concentrations (in terms of the total monitoring period) of 214Bi, 208Tl, 228Ac and 40K were found to be 108 ± 30, 57 ± 14, 40 ± 5 and 9800 ± 500 Bqm−3, respectively. As concerns the quantification of 137Cs, the average activity concentration in terms of the total monitoring period (although it is uncertain) was found to be 6 ± 4 Bqm−3. The data analysis proved that the system had a stable operation in terms of voltage stability, so all acquired spectra could be summed up efficiently in time to produce statistically optimal gamma-ray spectra for further analysis. Full article
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<p>The study area (COSYNA—Helgoland cabled network). The radioactivity system was deployed in the cabled North Sea Observatory.</p>
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<p>The GeoMAREA detection system placed in a special frame and integrated in the COSYNA cabled network (North Sea Observatory, Helgoland station). The crystal is at the top, in the direction of the sea surface. The two connectors for power and communication are also depicted.</p>
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<p>The experimental set-up for the deployment of the GeoMAREA sensor at Helgoland Island, North Sea.</p>
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<p>The time series of total counting rate, as acquired from the GeoMAREA detection system. The time lag of the measurement is 3 h.</p>
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<p>The total gamma-ray spectrum acquired by the GeoMAREA system during the acquisition period. The gamma-ray intensity (counts) plotted versus channels (raw data, bottom axis) and keV (energy calibrated, top axis). The energy peaks between 600 and 800 channels are due to the intrinsic radiation of the crystal CeBr<sub>3</sub>.</p>
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<p>The analysis [<a href="#B21-jmse-13-00516" class="html-bibr">21</a>] procedure for the folded photopeaks of <sup>214</sup>Bi, <sup>208</sup>Tl and <sup>137</sup>Cs with low statistical significance (at 609, 583 and 661 keV, respectively). The <sup>137</sup>Cs contribution is very small and is not evident in the graph.</p>
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<p>The analysis [<a href="#B21-jmse-13-00516" class="html-bibr">21</a>] procedure for the folded photopeaks of <sup>214</sup>Pb with low statistical significance (at 291 and 351 keV, respectively). The depicted counts include the contributions of <sup>228</sup>Ac and <sup>212</sup>Pb.</p>
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<p>Rainfall data in lm<sup>−2</sup> for the monitoring period. The data are averaged for each day.</p>
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23 pages, 4134 KiB  
Article
Towards a Common Language for Mainstreaming Nature-Based Solutions Through Coastal Systems in the North Sea Region: The Manabas Coast Project
by Geert J. M. van der Meulen, Jurre J. de Vries, Lisa van Well and Frances A. Kannekens
J. Mar. Sci. Eng. 2025, 13(3), 509; https://doi.org/10.3390/jmse13030509 - 5 Mar 2025
Viewed by 198
Abstract
Nature-based solutions (NBSs) offer an opportunity to address environmental and societal challenges worldwide while simultaneously providing benefits for human well-being as well as biodiversity. Despite a growing demand and evidence base for NBSs in coastal systems, the scaling of their implementation and mainstreaming [...] Read more.
Nature-based solutions (NBSs) offer an opportunity to address environmental and societal challenges worldwide while simultaneously providing benefits for human well-being as well as biodiversity. Despite a growing demand and evidence base for NBSs in coastal systems, the scaling of their implementation and mainstreaming of their principles in policy and practice are constrained by multiple barriers, such as misinterpretations of concepts, effectiveness, or locked-in preferences or conventions of traditional solutions. To address these constraints, an international consortium of coastal authorities and experts in the North Sea Region collaborates to validate, document, and share learnings of NBSs to establish a framework for mainstreaming NBSs for flood and coastal erosion risk management around the North Sea. Co-creation processes of workshops, field visits, and expert knowledge sessions contributed to a theoretical framework and baseline assessments of exemplary sandy and muddy case study sites in the region, amongst others, iteratively providing and showcasing building blocks for the mainstreaming framework. This article takes stock halfway of the project’s activities, learnings, and status of the called-for common language. Full article
(This article belongs to the Special Issue Nature-Based Solutions in Coastal Systems)
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<p>Manabas Coast case study sites.</p>
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<p>Multi-level perspective on transitions adapted from [<a href="#B35-jmse-13-00509" class="html-bibr">35</a>] (with permission from Elsevier, 2025) for NBSs.</p>
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<p>Alternative representation and conceptualization of the multi-level perspective adapted from [<a href="#B35-jmse-13-00509" class="html-bibr">35</a>] (with permission from Elsevier, 2025) to represent (<b>a</b>) Manabas Coast as a niche regime, (<b>b</b>) its case studies as niche experiments, and (<b>c</b>) the positioning of mainstreaming and (<b>d</b>) scaling.</p>
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<p>Visual synthesis infographic of mainstreaming and scaling of NBSs across natural, social, and governance systems.</p>
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<p>Manabas Coast mainstreaming framework.</p>
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23 pages, 2887 KiB  
Article
Red Mullet (Mullus barbatus) Collected from North and South Euboean Gulf, Greece: Fishing Location Effect on Nutritive Quality
by Roxana-Georgiana Nita, Vassilis Athanasiadis, Dimitrios Kalompatsios, Martha Mantiniotou, Aggeliki Alibade, Chrysanthi Salakidou and Stavros I. Lalas
Fishes 2025, 10(3), 115; https://doi.org/10.3390/fishes10030115 - 5 Mar 2025
Viewed by 245
Abstract
Red mullet (Mullus barbatus), a prominent fish species in the Mediterranean Sea, is a fish with a particular abundance of unsaturated fatty acids and other nutrients, including a substantial quantity of minerals. The nutritive quality parameters (lipid quality indices, fatty acid [...] Read more.
Red mullet (Mullus barbatus), a prominent fish species in the Mediterranean Sea, is a fish with a particular abundance of unsaturated fatty acids and other nutrients, including a substantial quantity of minerals. The nutritive quality parameters (lipid quality indices, fatty acid profiles, and mineral content, along with proximate composition) of 75 red mullet samples collected from five distinct locations (L1–L5) in the North and South Euboean Gulf, Euboea Island (Evia), Greece, were examined. It was hypothesized that the different habitats may have an impact on each fish’s chemical composition. Proximate composition (protein, ash, moisture, fat, and minerals) and bioactive compound determination (total carotenoids, and vitamins A, E, and C) were conducted on the lyophilized fish samples. The protein and lipid content of the wet fillet varied substantially from 10.8 to 14.3 and 13.2 to 16.8% w/w, respectively. The samples exhibited statistically non-significant variation in the total SFAs and MUFAs (p > 0.05). The level of total PUFAs was above 30% in all the samples and no significant differences were observed between them. However, arachidonic acid (20:4 ω-6) was only detected in fish samples from two locations (i.e., L1 and L3). The concentrations of Fe, Na, Mg, K, Ca, Ag, Sr, Li, and Zn varied significantly (p < 0.05) in relation to the size of the fish samples. The highest concentrations of heavy metals were detected at the northern location (L5), indicating a possible negative correlation between size and arsenic concentration. The varied mineral composition and fatty acid content of the samples can be attributed to their distinctive biological characteristics (i.e., length and weight) and dietary environments. Full article
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Graphical abstract

Graphical abstract
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<p>Map of different fishing locations (L1–L5) in the Euboean Gulf.</p>
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<p>Graphical illustration of the conducted assays in the red mullet fillets.</p>
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<p>Consensus map of the measured parameters in blocks between five distinct locations of fish samples. Inertia values are shown with gold arrows (dashed line) to highlight their direction and magnitude for better interpretation.</p>
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<p>Block partial contributions plot between measured parameters in blocks.</p>
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21 pages, 4695 KiB  
Article
Architecture and Genesis of Submarine Migrating Channel–Levee Systems in the Pearl River Mouth Basin, Northern South China Sea
by Zenggui Kuang, Zijian Zhang, Jinfeng Ren and Wei Deng
J. Mar. Sci. Eng. 2025, 13(3), 505; https://doi.org/10.3390/jmse13030505 - 5 Mar 2025
Viewed by 149
Abstract
Seismic data reveal that the shelf edge of the Pearl River Mouth Basin in the northern South China Sea is characterized by slope channels that have consistently migrated in a north-easterly direction over millions of years. Previous research suggests that the channel migration [...] Read more.
Seismic data reveal that the shelf edge of the Pearl River Mouth Basin in the northern South China Sea is characterized by slope channels that have consistently migrated in a north-easterly direction over millions of years. Previous research suggests that the channel migration is driven by the interplay between along-slope bottom currents and downslope turbidity currents. Here, we propose an alternative interpretation, suggesting the migrating channels are actually a series of channel–levee systems and the migration is driven by their own evolution of erosion–deposition under the influence of the Coriolis force. A detailed interpretation of high-resolution seismic data reveals seven types of architectural elements, characteristic of channel–levee systems, which are erosional bases, outer levees, inner levees, channel-axis fills, marginal slumps, drapes, and lobes. An analysis of the sequence stratigraphy and stacking pattern of channels suggests that channel migration from the middle Miocene to the present is discontinuous with at least three regional discontinuities within the channel migration sequence marked by regional drapes. Down-dipping reflections along the margin of channels, previously interpreted as bottom-currents deposits, are here reinterpreted as mass-transport processes along steep channel walls. The migration is most prominent in the middle reach, where erosion and deposition coexist and dominate alternately in two different phases. During the long-term canyon-filling turbidity currents prevailing phase, deposition dominates, leading to the development of a prominent asymmetric right-hand (west) inner levee due to the Coriolis force. In contrast, during the canyon-flushing turbidity currents prevailing phase, erosion dominates and the preferred right-hand (west) inner levee enforces the flow to erode eastward, then drives the channel migrating eastward. The alternating effects of erosion and deposition ultimately result in unidirectional channel migration. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Major structural units on the northern slope of South China Sea. Red rectangle <span class="html-italic">a</span> shows the region where migrating channels were first reported in the northern SCS [<a href="#B5-jmse-13-00505" class="html-bibr">5</a>] and is the study area of this paper, as shown in <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>a. The migrating channels developed intermittently from the late Miocene to the Quaternary on the northern slope of Baiyun Sag, PRMB, with a present water depth ranging from 450 m to 1500 m. The blue rectangle <span class="html-italic">b</span> on the shelf margin of Qiogdongnan Basin indicates another area where migrating channels occurred in the late Miocene with a present water depth of around 250 m [<a href="#B19-jmse-13-00505" class="html-bibr">19</a>].</p>
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<p>(<b>a</b>) Multi-beam bathymetry map showing channelized morphologies and locations of the 2D seismic profiles shown in Figures 3–6. See <a href="#jmse-13-00505-f001" class="html-fig">Figure 1</a> for map location. (<b>b</b>) The enlarged map shows the major channels studied in the paper. Also shown are the locations of the seismic profiles displayed in Figure 7.</p>
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<p>(<b>a</b>) Uninterpreted and (<b>b</b>) interpreted seismic profile located in the middle reach of the CLSs showing major architecture elements including the erosional base, outer levee, inner levee, channel-axis fills (D-C HARs, discontinuous and chaotic high-amplitude reflectors), marginal slump, and drape. Note the outer levee, shown in the shape of an irregular triangle, and inner levee, indicating multiple stepped parallelograms, and wedge-shaped or irregular geometries. See <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>a for the profile location.</p>
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<p>(<b>a</b>) Uninterpreted and (<b>b</b>) interpreted seismic profile crossing the lower reach of the CLSs showing some of the major architecture elements, including the outer levee, marginal slump, channel-axis fills (C-P HARs, continuous-parallel high-amplitude reflectors), and lobe. Note the truncation between the lobe and surrounding deposits. See <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>a for the profile location.</p>
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<p>Sequence stratigraphic framework in the upper-middle reach of the CLSs. Three seismic sequences, SQA, SQB and SQC, bounded by the regional erosive surface (sequence boundaries), T1, T2, T3, and modern seafloor, respectively, are identified. Note the discontinuity in the migration path between SQC and SQB, and the lack of an underlying old channel below one of the channels. See <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>a for the profile location.</p>
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<p>Sequence stratigraphic framework in the lower reach of the CLSs. Note that the upper-reach CLSs developed in SQC (<a href="#jmse-13-00505-f005" class="html-fig">Figure 5</a>) are now represented by lobe deposits. The morphology of CLSs within SQB is maintained, but the lower-reach CLSs are far more depositional than the corresponding upper-reach CLSs. See <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>a for the profile location.</p>
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<p>Stacking architecture and migration characteristics of channel CC1. (<b>a</b>) Distribution of channel CC1 and three related overlying channels: CB1, CB2, and CB3 and location of seismic profiles of <a href="#jmse-13-00505-f007" class="html-fig">Figure 7</a>d–j. Note the different flow direction of CC1 and other channels developed in SQB. (<b>b</b>,<b>c</b>) The erosion–deposition parameters of CC1. Note that the longest lateral migration and thickest vertical aggradation occurred in the upper–lower middle reach. LMD—lateral migration distance; VAT—vertical aggradation thickness; LER—lateral erosive rate; SR—sedimentation rate. (<b>d</b>–<b>j</b>) Seismic profiles across CC1 from the uppermost to lowermost positions of the available seismic data. Note that the channel located on the top of CC1 along the migration route changed from CB3 (<b>d</b>) to CB2 (<b>e</b>), then to CB1 (<b>f</b>–<b>h</b>), and the most prominent migrating features occurred within the upper–lower middle reach of the channel. See <a href="#jmse-13-00505-f002" class="html-fig">Figure 2</a>b for the profile location.</p>
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<p>Generalized schematic drawing showing the genesis of the channel migration and aggradation. (<b>a</b>–<b>g</b>) indicate the evolutionary stage of the migrating channel-levee system. See text for details.</p>
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28 pages, 7435 KiB  
Article
Climatological and Hydrological Extremes of the Andaman and Nicobar Islands, India, and Its Database for Public Users
by Abhilash, Anurag Satpathi, Talaviya Harshangkumar, Thangavel Subramani, Iyyappan Jaisankar and Namendra Kumar Shahi
Atmosphere 2025, 16(3), 301; https://doi.org/10.3390/atmos16030301 - 4 Mar 2025
Viewed by 1432
Abstract
The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding [...] Read more.
The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding of its climatic patterns. In light of this, this study provides a comprehensive analysis of spatiotemporal variability and trends in mean and extreme precipitation across the Andaman and Nicobar Islands using long-term (i.e., 1981–2023) high-resolution Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Our findings indicate a significant increase in monsoonal precipitation, particularly in South Andaman, where the mean precipitation trend is 11.10 mm/year, compared to 6.54 mm/year in Nicobar. Light-to-moderate precipitation events occur more frequently than heavy precipitation across all districts, although heavy precipitation is more frequent in Andaman than in Nicobar. Significant decadal increases in light-to-moderate precipitation events are found across most of Nicobar, while parts of Andaman showed a rise in the frequency of moderate-to-heavy precipitation events. Trend analysis of the highest single-day precipitation annually reveals mixed patterns, with increases noted in North and Middle Andaman (3.66 mm per decade) and South Andaman (1.13 mm per decade), while Nicobar shows a slight decrease (−0.63 mm per decade). Maximum consecutive five-day precipitation trends indicate significant annual increases in North and Middle Andaman (14.98 mm per decade) and South Andaman (3.49 mm per decade), highlighting the variability in extreme precipitation events. The observed trends in precipitation and its extremes highlight the heterogeneity of precipitation patterns, which are critical for water resource management, agriculture, and disaster risk mitigation in the region, particularly in the context of increasing precipitation variability and intensity driven by climate change. Further investigation is needed to understand the physical mechanisms driving the increase in frequency and intensity of precipitation, which will be addressed in a separate paper. Full article
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<p>Location map of the study area (<b>a</b>) with original geography: (<b>b</b>) elevation (meters) from SRTM DEM (1 arc-second) and (<b>c</b>) Land Use/Land Cover (LULC) classification.</p>
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<p>Mean seasonal long-term distribution of precipitation (in mm; left panel) and corresponding time series of absolute seasonal precipitation (right panel) during (<b>a</b>) winter, (<b>b</b>) pre-monsoon, (<b>c</b>) monsoon, and (<b>d</b>) post-monsoon for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India. The left panel illustrates the spatial distribution of precipitation across each season, while the right panel shows the temporal variation and trends in seasonal precipitation amounts over the 42-year period.</p>
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<p>Pixel-level representation of the Coefficient of Variation (%) for seasonal precipitation during (<b>a</b>) winter, (<b>b</b>) pre-monsoon, (<b>c</b>) monsoon, and (<b>d</b>) post-monsoon for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India. This figure highlights the spatial variability in precipitation across seasons, with higher coefficients indicating greater variability and inconsistency in seasonal precipitation patterns.</p>
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<p>Year-wise categorization of seasonal precipitation based on IMD classification during (<b>a</b>) winter, (<b>b</b>) pre-monsoon, (<b>c</b>) monsoon, and (<b>d</b>) post-monsoon for different districts during the period of 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of Sen’s slope (in mm per year; left panel) indicating the trend magnitude and the <span class="html-italic">p</span>-value of the Mann–Kendall test (right panel) showing the statistical significance of the trends during (<b>a</b>) winter, (<b>b</b>) pre-monsoon, (<b>c</b>) monsoon, and (<b>d</b>) post-monsoon at the pixel level for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India. The Nicobar district displayed the highest mean precipitation trend of 2.91 mm/year, with pixel-level values ranging from 1.39 mm/year to 4.10 mm/year, indicating a consistent increase. The North and Middle Andaman district showed a moderate mean trend of 0.51 mm/year, with values spanning from 0.06 mm/year to 1.39 mm/year. In the South Andaman district, the mean trend was 0.44 mm/year, with a range of 0.03 mm/year to 1.76 mm/year, reflecting relatively minor changes. Trend significance analysis using the Mann–Kendall test revealed that 2.63% of grids across the islands had significant trends at the 99% confidence interval, while 26.97% were significant at the 95% confidence level. Within the Andaman group, 0.72% and 7.88% of grids exhibited significance at 99% and 95% confidence levels, respectively, while the Nicobar group showed higher significance, with 1.91% and 18.85% of grids meeting the same thresholds.</p>
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<p>Spatial distribution of (<b>a</b>) mean total annual precipitation (PRCPTOT) in mm, representing the climatology of precipitation, (<b>b</b>) Sen’s slope (in mm per year), indicating the magnitude of precipitation trends, and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, which assesses the statistical significance of these trends, at the pixel level, for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of (<b>a</b>) mean annual rainy days, illustrating the climatology of the number of rainy days per year, (<b>b</b>) Sen’s slope (in days per decade), indicating the magnitude of trends in rainy days, and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends, at the pixel level, for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of (<b>a</b>) mean annual rainy days, illustrating the climatology of the number of rainy days per year, (<b>b</b>) Sen’s slope (in days per decade), indicating the magnitude of trends in rainy days, and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends across various precipitation intensity categories: light precipitation (2.5 mm to 15.5 mm), moderate precipitation (15.6 mm to 64.4 mm), heavy precipitation (64.5 mm to 115.5 mm), and very heavy precipitation (greater than 115.6 mm).</p>
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<p>Spatial distribution of (<b>a</b>) mean annual maximum length of dry spell (CDD) and wet spell (CWD) in days, representing the climatology of the maximum number of consecutive days with precipitation less than 2.5 mm and exceeding 2.5 mm per year, respectively; (<b>b</b>) Sen’s slope (in days per decade), indicating the magnitude of trends in CDDs and CWDs; and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends at the pixel level for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of (<b>a</b>) mean annual maximum 1-day precipitation (Rx1day; in mm) and consecutive 5-day precipitation (Rx5day; in mm), illustrating the climatology of the highest recorded precipitation on a single day each year and wettest consecutive 5-day precipitation periods, respectively; (<b>b</b>) Sen’s slope (in mm per decade), indicating the magnitude of trends in Rx1day and Rx5day; and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends at the pixel level for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of (<b>a</b>) mean annual simple precipitation intensity index (SDII), illustrating the climatology of precipitation intensity on wet days (defined as days with precipitation ≥ 2.5 mm) in mm; (<b>b</b>) Sen’s slope (in mm per decade), indicating the magnitude of trends in SDII; and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends at the pixel level for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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<p>Spatial distribution of (<b>a</b>) mean annual total precipitation when daily precipitation exceeds the 95th percentile (R95pTOT; % of PRCPTOT) and 99th percentile (R99pTOT; % of PRCPTOT), illustrating the climatology of the contribution to total precipitation from very and extremely wet days, respectively; (<b>b</b>) Sen’s slope (% of PRCPTOT per decade), indicating the magnitude of trends in R95pTOT and R99pTOT, respectively; and (<b>c</b>) <span class="html-italic">p</span>-value of the Mann–Kendall test, indicating the statistical significance of these trends at the pixel level for the period 1981–2023 in the Andaman and Nicobar Islands, Union Territory of India.</p>
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28 pages, 72675 KiB  
Article
Geochemical and Isotopic Features of Geothermal Fluids Around the Sea of Marmara, NW Turkey
by Francesco Italiano, Heiko Woith, Luca Pizzino, Alessandra Sciarra and Cemil Seyis
Geosciences 2025, 15(3), 83; https://doi.org/10.3390/geosciences15030083 - 1 Mar 2025
Viewed by 241
Abstract
Investigations carried out on 72 fluid samples from 59 sites spread over the area surrounding the Sea of Marmara show that their geochemical and isotopic features are related to different segment settings of the North Anatolian Fault Zone (NAFZ). We collected fluids from [...] Read more.
Investigations carried out on 72 fluid samples from 59 sites spread over the area surrounding the Sea of Marmara show that their geochemical and isotopic features are related to different segment settings of the North Anatolian Fault Zone (NAFZ). We collected fluids from thermal and mineral waters including bubbling and dissolved gases. The outlet temperatures of the collected waters ranged from 14 to 97 °C with no temperature-related geochemical features. The free and dissolved gases are a mixture of shallow and mantle-derived components. The large variety of geochemical features comes from intense gas–water (GWI) and water–rock (WRI) interactions besides other processes occurring at relatively shallow depths. CO2 contents ranging from 0 to 98.1% and helium isotopic ratios from 0.11 to 4.43 Ra indicate contributions, variable from site to site, of mantle-derived volatiles in full agreement with former studies on the NAFZ. We propose that the widespread presence of mantle-derived volatiles cannot be related only to the lithospheric character of the NAFZ branches and magma intrusions have to be considered. Changes in the vertical permeability induced by fault movements and stress accumulation during seismogenesis, however, modify the shallow/deep ratio of the released fluids accordingly, laying the foundations for future monitoring activities. Full article
(This article belongs to the Section Geochemistry)
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Figure 1

Figure 1
<p>Map of historic earthquakes in the wider Marmara region compiled from various sources [<a href="#B17-geosciences-15-00083" class="html-bibr">17</a>,<a href="#B18-geosciences-15-00083" class="html-bibr">18</a>,<a href="#B19-geosciences-15-00083" class="html-bibr">19</a>,<a href="#B20-geosciences-15-00083" class="html-bibr">20</a>]. Labels indicate the year of the event for magnitudes M ≥ 7. White lines depict active faults according to the General Directorate of Mineral Research and Exploration (MTA) [<a href="#B21-geosciences-15-00083" class="html-bibr">21</a>]; off-shore faults are taken from Armijo et al. (2002) [<a href="#B14-geosciences-15-00083" class="html-bibr">14</a>]. Orange and red lines indicate the ruptures related to the Ganos earthquake of 1912 and the Izmit/Düzce events of 1999, respectively.</p>
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<p>Map of fluid sampling sites around the Sea of Marmara. Symbols indicate color-coded water temperatures. Small white circles depict sites with bubbling gases. Values are sample numbers used in this study (see <a href="#geosciences-15-00083-t001" class="html-table">Table 1</a>). Names of geographic areas investigated are given.</p>
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<p>Piper diagram of the water samples as a function of the geographical areas. Sample labels as the ID numbers in <a href="#geosciences-15-00083-t002" class="html-table">Table 2</a>.</p>
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<p>Ca vs Mg (<b>a</b>) and HCO<sub>3</sub> (<b>b</b>). The occurrence of GWI processes allows CO<sub>2</sub> dissolution that is responsible for the observed geochemical features related to WRI resulting in dolomite and calcite dissolution to various extents. Sample labels are the same as the ID numbers in <a href="#geosciences-15-00083-t002" class="html-table">Table 2</a>.</p>
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<p>Na vs HCO<sub>3</sub> (<b>a</b>) and Na vs. Cl (<b>b</b>). The occurrence of WRI and GWI processes is responsible for the observed geochemical features. Blue star symbol = sea water. Sample labels are the same as the ID numbers in <a href="#geosciences-15-00083-t002" class="html-table">Table 2</a>. Symbol colors are as shown in <a href="#geosciences-15-00083-f003" class="html-fig">Figure 3</a>.</p>
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<p>Ca-SO<sub>4</sub> plot showing that gypsum dissolution is not the main process responsible for the SO<sub>4</sub> ions, with the water chemistry being a consequence of WRI and GWI processes. Sample labels are the same as the ID numbers in <a href="#geosciences-15-00083-t002" class="html-table">Table 2</a>. SW = sea water.</p>
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<p>δ<sup>18</sup>O–δD plot for the collected waters. Samples fall between the two reference lines representing the EMMWL (Eastern Mediterranean Meteoric Water Line; Hatvani et al., 2023 [<a href="#B62-geosciences-15-00083" class="html-bibr">62</a>]) and the GMWL (Global Meteoric Water Line; Rozanski et al., 1993 [<a href="#B63-geosciences-15-00083" class="html-bibr">63</a>]). BMWL refers to the Bursa local meteoric water line proposed by Imbach et al. (1997) [<a href="#B38-geosciences-15-00083" class="html-bibr">38</a>]. Sample labels are the same as the ID numbers in <a href="#geosciences-15-00083-t002" class="html-table">Table 2</a>.</p>
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<p>CO<sub>2</sub>-N<sub>2</sub> relationships for bubbling (filled circles) and dissolved (diamond) gases indicating the presence of two end members in the gas phase, namely the shallow atmospheric-derived N<sub>2</sub> component and the deep-originated CO<sub>2</sub>, vented over the Marmara area that mix at variable extents. Numbers indicate the sample IDs as in <a href="#geosciences-15-00083-t001" class="html-table">Table 1</a>.</p>
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<p>CO<sub>2</sub>-CH<sub>4</sub>-N<sub>2</sub> triangular diagram of the bubbling (filled circles) and dissolved (diamonds) gases showing the relative contents of the three end members N<sub>2</sub>, CO<sub>2</sub> and CH<sub>4</sub>. We plotted the N<sub>2</sub> excess with respect to the atmospheric nitrogen. The arrows highlight the GWI processes (CO<sub>2</sub> loss and increased N<sub>2</sub> and CH<sub>4</sub> contents) as well as mixings due to CO<sub>2</sub> addition from various sources that significantly changed the composition of the pristine gas phase. The numbers beside the symbols indicate the site as listed in <a href="#geosciences-15-00083-t001" class="html-table">Table 1</a>.</p>
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<p>CO<sub>2</sub> content vs δ<sup>13</sup>C<sub>CO2</sub> for the bubbling gases (<b>a</b>) and for δ<sup>13</sup>C<sub>TDIC</sub> of the dissolved gases (<b>b</b>). The plots depict a clear direct correlation between isotopic ratios and CO<sub>2</sub> and HCO<sub>3</sub> contents. The contemporary trends denote the fractionation with quantitative loss of gaseous CO<sub>2</sub> and its heavy isotope as well as the occurrence of further fractionation processes. The occurrence of similar trends followed by samples from different sites around the Marmara area suggests that the vented CO<sub>2</sub> is not solely controlled by shallow interactions with groundwaters, and that the coexistence of multiple sources has to be considered.</p>
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<p>Helium isotopic ratios (uncorrected R/Ra values) and <sup>4</sup>He/<sup>20</sup>Ne relationships for both dissolved and bubbling gases. The theoretical lines represent binary mixing trends of atmospheric helium with mantle-originated and crustal helium. The assumed end members for He-isotopic ratios and <sup>4</sup>He/<sup>20</sup>Ne ratios are ASW (1 Ra, He/Ne = 0.267: Holocer et al., 2002) [<a href="#B49-geosciences-15-00083" class="html-bibr">49</a>]; 8Ra for a MORB-type mantle; and 3.5 Ra for contaminated mantle; crust 0.05Ra and <sup>4</sup>He/<sup>20</sup>Ne ratio = 10,000. Filled circles = bubbling gases; filled diamonds = dissolved gases. Sample IDs are as reported in <a href="#geosciences-15-00083-t003" class="html-table">Table 3</a>. All error bars are within the symbol size.</p>
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<p>CO<sub>2</sub>/<sup>3</sup>He–<sup>4</sup>He. The plot shows how the vented gases are a mixture of two main components: magmatic-type and crustal-originated. Circles = bubbling gases; diamonds = dissolved gases. The arrows display the main trends affecting the composition of the gas phase.</p>
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<p>Map showing locations mentioned in the text. Numbers refer to sampling sites of this study (see <a href="#geosciences-15-00083-t001" class="html-table">Table 1</a>).</p>
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<p>Chemical composition of thermal and mineral waters around the Sea of Marmara. The diameter of the pies scales with the specific electrical conductivity of the waters. Small circles in the centre of the pies indicate the water temperature: blue—cold (&lt;20 °C); orange—hot (&gt;40 °C).</p>
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<p>Gas composition of thermal and mineral waters around the Sea of Marmara. Small white circles in the centre of the pies indicate bubbling gases.</p>
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<p>Helium isotope ratios given in R/Ra at mineral and thermal waters around the Sea of Marmara. Light purple areas depict Tertiary volcanic rocks, hatched areas mark intrusive igneous rocks of Paleozoic to Cenozoic age. Light and dark gray areas indicate Mesozoic and Paleozoic rocks, respectively. White areas are Paleogene to Quaternary sediments. Simplified geology modified from Pawlewicz et al. (1997) [<a href="#B83-geosciences-15-00083" class="html-bibr">83</a>].</p>
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13 pages, 8409 KiB  
Article
Mapping Storm Surge Risk at County Level in Coastal Areas of China
by Xianwu Shi, Yande Zhang, Shan Liu, Lifen Yang, Lanlan Yu, Yao Zhang, Ning Jia and Zilu Tian
J. Mar. Sci. Eng. 2025, 13(3), 427; https://doi.org/10.3390/jmse13030427 - 25 Feb 2025
Viewed by 122
Abstract
Storm surges represent a prominent and significant natural hazard in the coastal areas of China and cause substantial human and economic losses. We investigated historical storm surge events in these areas to assess the distribution of associated hazards and to construct a storm [...] Read more.
Storm surges represent a prominent and significant natural hazard in the coastal areas of China and cause substantial human and economic losses. We investigated historical storm surge events in these areas to assess the distribution of associated hazards and to construct a storm surge hazard index. The tide-gauge data from 83 observational stations along the Chinese coast were collected, and the assessment was based on two indicators, namely the storm surge height and the exceeded water warning level of these events. Further, we conducted a vulnerability assessment of coastal counties in China using population and economic distribution data. Thereafter, the distribution of storm surge hazards and vulnerability levels was considered, and we determined the county-level risk of storm surges covering 219 coastal counties in China. The findings revealed substantial spatial variations therein, with high-risk areas in terms of the population and economic effects of such surges accounting for 25.1% (55/219) and 27.4% (60/219) of all coastal counties, respectively. These results provide preliminary insight into storm surge risks in China and have implications for the prevention and mitigation of storm surges for central government. Full article
(This article belongs to the Section Marine Hazards)
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<p>Distribution of tide-gauge stations along coast of China.</p>
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<p>Spatial distribution map of four-color water warning levels: (<b>a</b>) Blue; (<b>b</b>) Yellow; (<b>c</b>) Orange; (<b>d</b>) Red.</p>
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<p>Distribution of storm surge hazards in coastal counties of China.</p>
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<p>Storm surge vulnerability of coastal counties in China according to population (<b>a</b>) and GDP (<b>b</b>).</p>
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<p>Storm surge risk for coastal counties according to population (<b>a</b>) and GDP (<b>b</b>).</p>
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16 pages, 10543 KiB  
Article
Eocene Gravity Flows in the Internal Prebetic (Betic Cordillera, SE Spain): A Vestige of an Ilerdian Lost Carbonate Platform in the South Iberian Margin
by Josep Tosquella, Manuel Martín-Martín, Crina Miclăuș, José Enrique Tent-Manclús, Francisco Serrano and José Antonio Martín-Pérez
Geosciences 2025, 15(3), 81; https://doi.org/10.3390/geosciences15030081 - 23 Feb 2025
Viewed by 351
Abstract
In the Betic-Rif Cordilleras, recent works have evidenced the existence of well-developed Eocene (Ypresian-Bartonian) carbonate platforms rich in Larger Benthic Foraminifera (LBF). Contrarily to other sectors of the western Tethys, like the Pyrenean domain in the North Iberian Margin, where these platforms started [...] Read more.
In the Betic-Rif Cordilleras, recent works have evidenced the existence of well-developed Eocene (Ypresian-Bartonian) carbonate platforms rich in Larger Benthic Foraminifera (LBF). Contrarily to other sectors of the western Tethys, like the Pyrenean domain in the North Iberian Margin, where these platforms started in the early Ypresian (Ilerdian), in the Betic-Rif chains, the recorded Eocene platforms started in the late Ypresian (Cuisian) after a widespread gap of sedimentation including the Ilerdian time span. In this work, the Aspe-Terreros Prebetic section (External Betic Zone) is studied. An Eocene succession with gravity flow deposits consisting of terrigenous and bioclastic turbidites, as well as olistostromes with olistoliths, was detected. In one of these turbidites, we dated (with the inherent limitations when dating bioclasts contained by gravity flow deposits) the middle Ilerdian, on the basis of LBF, representing a vestige of a missing Illerdian carbonate platform. The microfacies of these turbidites and olistoliths rich in LBF have been described and documented in detail. The gap in the sedimentary record and absence of Ilerdian platforms in the Betic-Rif Cordillera have been related to the so-called Eo-Alpine tectonics (Cretaceous to Paleogene) and sea-level variations contemporarily with the establishment of shallow marine realms in the margins of the western Tethys. Full article
(This article belongs to the Section Sedimentology, Stratigraphy and Palaeontology)
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Figure 1
<p>(<b>A</b>) Tectonic map of the Betic Cordillera (after [<a href="#B7-geosciences-15-00081" class="html-bibr">7</a>]) showing the locations of the Aspe-Terreros section; (<b>B</b>) Alpine Chain map of the western-central Mediterranean area (modified from [<a href="#B5-geosciences-15-00081" class="html-bibr">5</a>]).</p>
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<p>(<b>A</b>) Geological sketch map of the Aspe-Terreros area superimposed onto the aerial picture obtained by drone (the photo sets obtained during the drone flights were processed with WebODM software, obtaining 263 aligned pictures used to build a 3D model for an area of about 0.3145 km<sup>2</sup>, 1,177,191 tie points, a dense point cloud of 24,805,637 points, and an average Ground Sampling Distance of 2.2 cm). The orthophotography obtained has a size of 15,009 × 14,425 pixels, showing the three main intervals (with locations of the measured section and the collected samples). (<b>B</b>) Stratigraphic column of the Aspe-Terreros area, showing the three main intervals, sedimentologic characteristics, and sample locations. (<b>C</b>) Aerial picture of the Aspe-Terreros studied section obtained by drone. In (<b>A</b>–<b>C</b>), dotted blue lines contour the second interval (olistostrome), while dotted white lines in the second interval contour the olistoliths. Legend for 2B: mst—mudstone; sst—sandstone; rst—rudstone; blk—blocks (olistoliths).</p>
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<p>Field photos of the Aspe-Terreros section (<b>A</b>–<b>H</b> from bottom to top) with the location of the main samples (all samples were taken following standard procedures after removing the surficial cover). (<b>A</b>) Bioclastic turbidite in the first interval of the section (samples EP80a and EP80b); (<b>B</b>) preserved stratification of the olistostrome in the second interval of the section (sample EP82); (<b>C</b>,<b>D</b>) olistoliths in the second interval of the section; (<b>E</b>) large olistoliths in the second interval of the section (samples EP83 to EP85); (<b>F</b>) mudstones from the third interval of the section (samples EP87 to EP89); (<b>G</b>) mudstones with bioclastic turbidite in the third interval of the section (samples EP92 and EP93); (<b>H</b>) dark greyish to greenish marls and clays in the top of the section (sample EP97).</p>
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<p>Significant planktonic foraminifera from the Aspe-Terreros section. Scale bar = 100 µm. <b>1a</b>: <span class="html-italic">Acarinina bullbrooki</span>, EP89. <b>2a</b>–<b>c</b>: <span class="html-italic">Acarinina cuneicamerata</span>, EP88. <b>3a</b>–<b>c</b>: <span class="html-italic">Acarinina praetopilensis</span>, EP87. <b>4a</b>–<b>c</b>: <span class="html-italic">Morozovella aragonensis</span>, EP88. <b>5a</b>–<b>b</b>: <span class="html-italic">Morozovella crater</span>, EP88. <b>6</b>: <span class="html-italic">Globigerinatheka subconglobata</span>, EP88. <b>7a</b>–<b>c</b>: <span class="html-italic">Morozovelloides crassatus</span>, EP87. <b>8a</b>–<b>c</b>: <span class="html-italic">Morozovelloides coronatus</span>, EP97. <b>9a</b>–<b>c</b>: <span class="html-italic">Subbotina eocaena</span>, EP97. <b>10a</b>–<b>c</b>: <span class="html-italic">Subbotina gortanii</span>, EP97. <b>11a</b>–<b>c</b>: <span class="html-italic">Igorina broedermanni</span>, EP88. <b>12a</b>–<b>c</b>: <span class="html-italic">Pseudohastigerina micra</span>, EP97. <b>13a</b>–<b>c</b>: <span class="html-italic">Turborotalia pomeroli</span>, EP97. Illustrated studied samples are stored and available, with their sampling numbers, for future research in the Ecology and Geology Department of the University of Málaga.</p>
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<p>Main LBF biomarker species of the late middle Ilerdian (SBZ8) from a bioclastic turbidite of the first interval of the Aspe-Terreros section (EP80a). <b>1</b>–<b>2</b>, <span class="html-italic">Nummulites spirectypus</span>: <b>1a</b>, BF internal view; <b>1b</b>, BF, external view; <b>2</b>, AF, internal view. <b>3</b>–<b>9</b>, <span class="html-italic">Nummulites atacicus</span>. <b>3</b>–<b>5</b>, BF, internal views; <b>6</b>–<b>9</b>, AF, internal views. <b>10</b>–<b>13</b>, <span class="html-italic">Nummulites exilis</span>: <b>10</b>, <b>11b</b>, BF internal views; <b>11a</b>, BF external view; <b>12</b>–<b>13</b>, AF internal views. <b>14</b>–<b>20</b>, <span class="html-italic">Nummulites globulus laxiformis</span>: <b>14</b>–<b>17</b>, BF internal views; <b>18</b>, AF external view; <b>19</b>–<b>20</b>, AA, internal views. <b>21</b>–<b>22</b>, <span class="html-italic">Assilina canalifera</span>: AF external views. <b>23</b>–<b>24</b>, <span class="html-italic">Assilina ammonea ammonea</span>: <b>23a</b>, AF external view; <b>23b</b>–<b>24</b>, AF internal views. Scale: 1 mm. AF: A-forms (gamonts); BF: B-forms (agamonts or schizonts). Illustrated studied samples, with their sampling numbers, are stored and available for future research in the Earth Sciences Department of the University of Huelva.</p>
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<p>Thin section photomicrographs of the microfacies recognized in the bioclastic turbidite and olistolith of the Aspe-Terreros section. (<b>A</b>–<b>D</b>) Microfacies 1 (sample EP80b). (<b>E</b>–<b>H</b>) Microfacies 2 (sample EP84). Scale: 1.0 mm. Key: a, <span class="html-italic">Alveolina</span>; as, <span class="html-italic">Assilina</span>; b, bryozoan; cc, crustose coralline; d, <span class="html-italic">Discocyclina</span>; ep, echinoid plate; es, echinoid spine; l, <span class="html-italic">Lenticulina</span>; m, miliolid; n, <span class="html-italic">Nummulites</span>; pf, planktonic foraminifera; q, quartz grain; ro, rotaliid; sbf, small benthic foraminifera.</p>
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<p>Paleogeographic section of the Eocene Prebetic platforms with the location of the Aspe-Terreros section and the hypothetic platform source of the gravity flow deposits (based on own data and [<a href="#B6-geosciences-15-00081" class="html-bibr">6</a>,<a href="#B25-geosciences-15-00081" class="html-bibr">25</a>,<a href="#B26-geosciences-15-00081" class="html-bibr">26</a>,<a href="#B27-geosciences-15-00081" class="html-bibr">27</a>,<a href="#B28-geosciences-15-00081" class="html-bibr">28</a>]).</p>
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16 pages, 3039 KiB  
Article
Bacterial Community Composition and Its Relationship with Environmental Factors in the Artificial Reef Area for Marine Ranching in Changhai County
by Jiamin Yan, Xu Wei, Liwei Si, Zheng Zhang, Jingsi Zhao, Liyu Deng, Tao Tian, Qingxia Li, Zengqiang Yin and Zhongxin Wu
Animals 2025, 15(5), 639; https://doi.org/10.3390/ani15050639 - 22 Feb 2025
Viewed by 181
Abstract
In this study, samples were collected from different types of artificial fish reefs and prevention and control areas in the sea areas of the northern part of Da Changshan Island and the northeastern part of Xiao Changshan Island in the North Yellow Sea. [...] Read more.
In this study, samples were collected from different types of artificial fish reefs and prevention and control areas in the sea areas of the northern part of Da Changshan Island and the northeastern part of Xiao Changshan Island in the North Yellow Sea. The purpose is to compare the differences in the bacterial communities among different regions, determine the impacts of environmental factors on the bacterial communities, and evaluate the ecological effects of artificial fish reefs on the marine bacterial communities. We obtained a total of 2,128,186 effective sequences and 4321 bacterial operational taxonomic units (OTUs), which were classified into 14 phyla and 76 genera. Proteobacteria were the most abundant phylum across the 32 samples, followed by Bacteroidetes. We found that all samples from the deep-sea control area exhibited the highest bacterial richness. In addition, all samples from the shallow-water concrete reef exhibited high community richness. The distribution of bacterial communities showed differences among different regions. In two specific sea areas, the bacteria in the sediment samples exhibited particularly remarkable characteristics of high diversity. Importantly, environmental factors significantly influence bacterial communities. In seawater samples, salinity (Sal) and dissolved oxygen (DO) were the primary factors affecting bacterial communities. Furthermore, grain size (GS) emerged as the most critical physicochemical factor influencing bacterial communities in sediment. This study compared the characteristics of bacterial communities in different types of artificial reefs and control areas in two marine ranches and revealed the main environmental factors affecting the bacterial communities. This is of great significance for protecting biodiversity and evaluating the ecological effects of artificial reef placement. Full article
(This article belongs to the Special Issue Global Fisheries Resources, Fisheries, and Carbon-Sink Fisheries)
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<p>Sampling station map in May 2023 (OCn: Shallow water control area; OSn: Shallow water stone reefs; OGn: Shallow water concrete component reefs; DCn: Deep water control area; DSn: Deep water stone reefs; DGn: Deep water concrete component reefs).</p>
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<p>The number of fungal OTUs specific to sample intersections. The left panel displays the number of OTUs in each sample. Samples with shared OTUs are connected by dots and lines in the dot matrix area. The bar chart in the top panel shows the number of shared OTUs at the intersections represented by the connecting dots.</p>
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<p>Four alpha diversity indices (ACE, Chao1, Shannon, and Pielou’s evenness) among the six groups. * Indicates that there are significant differences between the DS and OS groups.</p>
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<p>Principal component analysis based on the six groups.</p>
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<p>Relative abundance plot of bacterial communities at the gate level. Relative abundance plot of bacterial communities at the genus level. (In this figure, samples ending with “W” represent seawater samples, while those ending with “S” represent sediment samples.).</p>
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<p>(<b>A</b>) Redundancy analysis diagram of water environment factors. (<b>B</b>) Redundancy analysis diagram of sediment environmental factors. (<b>C</b>) Heatmap of correlation between bacterial community at genus level and sediment environmental factors. (<b>D</b>) Heatmap of correlation between bacterial community at the genus level and water environmental factors.</p>
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21 pages, 1732 KiB  
Article
PRV-1 Virulence in Atlantic Salmon Is Affected by Host Genotype
by Mark Polinski, Lynden Gross, David Groman, Marta Alarcón, Mark Braceland, Marije Booman, Delphine Ditlecadet, Samuel May, Nellie Gagné and Kyle Garver
Viruses 2025, 17(2), 285; https://doi.org/10.3390/v17020285 - 19 Feb 2025
Viewed by 310
Abstract
Heart and skeletal muscle inflammation (HSMI) is a significant disease affecting Atlantic salmon (Salmo salar) production in Norway but has had limited impact to production in North America. The causative agent of HSMI is piscine orthoreovirus genotype 1 (PRV-1), and disease [...] Read more.
Heart and skeletal muscle inflammation (HSMI) is a significant disease affecting Atlantic salmon (Salmo salar) production in Norway but has had limited impact to production in North America. The causative agent of HSMI is piscine orthoreovirus genotype 1 (PRV-1), and disease variation between regions is suggested to be at least partially driven by genetic variation of the virus. Using controlled laboratory injection challenges, we corroborate variations in disease outcomes for three PRV-1 isolates (PRV-1a from the eastern Pacific, PRV-1a from the western Atlantic, and PRV-1b from the Norwegian sea); however, virus replication dynamics, host recognition, and PRV-1-associated heart inflammation were also discrete relative to the Atlantic salmon stock challenged, irrespective of the viral isolate used. Specifically, New Brunswick Tobique River Atlantic salmon had less (p < 0.01) heart inflammation relative to Mowi-McConnell Atlantic salmon of Western Canada which, in turn, had less (p < 0.01) heart inflammation than Mowi Atlantic salmon of Scotland when cumulatively considering challenges using all three PRV-1 isolates. These data indicate that the presence of PRV-1a or PRV-1b alone is not sufficient to reliably predict disease and highlights at least one potential mechanism (host genotype) for reducing HSMI disease severity. Full article
(This article belongs to the Special Issue Aquatic Animal Viruses and Antiviral Immunity)
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<p>(<b>A</b>) Two-dimensional variance determined by a genetic principal component analysis of 50 k microarray genotypes among individual Atlantic salmon (n = 64) representing 4 geographically separate stains: British Columbia, Canada—Mowi-McConnell (BC salmon), European (Scotland) origin Mowi (EU salmon), New Brunswick, Canada—Tobique River (NB-TR salmon), and New Brunswick, Canada—Saint John River (NB-SJR salmon). (<b>B</b>) Tamura–Nei neighbor-joining phylogram indicating the genetic diversity of 48 concatenated PRV-1 genomes as presented by Siah et al. [<a href="#B36-viruses-17-00285" class="html-bibr">36</a>], as well as the 3 concatenated PRV-1 genomes sequenced in this study (highlighted with text) following Clustal Omega maximum-likelihood alignment. Phylogenetic groups (PRV-1b from Eastern Atlantic and Chile, PRV-1a from both Western and Eastern Atlantic, and PRV-1a from the Eastern Pacific) are indicated by branch lines color at 100% bootstrap consensus support. Scale bar indicates the genetic divergence as the average nucleotide substitutions per position.</p>
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<p>PRV-1 load, antiviral responsiveness, and inflammation in side-by-side challenge of BC Mowi-McConnell (BC) and New Brunswick St. John River (NB-SJR) Atlantic salmon. (<b>A</b>) Mean (line) and individual (dot) BC-PRV-1a L1 RNA loads measured 2–14 weeks post-challenge (wpc). Mean fold change (±SE) of (<b>B</b>) blood <span class="html-italic">mx1</span>, (<b>C</b>) blood <span class="html-italic">cd8a</span>, (<b>D</b>) blood <span class="html-italic">gzma</span>, (<b>E</b>) heart <span class="html-italic">mx1</span>, (<b>F</b>) heart <span class="html-italic">cd8a</span>, and (<b>G</b>) heart <span class="html-italic">gzma</span> transcripts relative to the mean of time-matched, strain-matched controls (SC); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 by two-way ANOVA and Šídák’s multiple comparisons tests of log-transformed fold changes; minimum twofold change suggestive of biological relevance is shaded. (<b>H</b>) The cumulative prevalence of epicarditis and endocarditis in hearts of control (SC) and BC-PRV-1a challenged (PRV) fish within 14 wpc. * <span class="html-italic">p</span> &lt; 0.05 by the Kruskal–Wallis and Dunn’s multiple comparisons tests.</p>
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<p>PRV-1 RNA loads of 72 individuals experiencing peak (6–10 wpc; left panels) or persistent (14 wpc; right panels) infections, categorized by either the strain of Atlantic salmon challenged (right component each panel) or the PRV-1 isolate administered (left component each panel). Mean (line) and individual (dot) PRV-1 L1 RNA loads identified in (<b>A</b>) whole blood, (<b>B</b>) the heart ventricle, or (<b>C</b>) plasma presented as the peak (eight highest recorded values 6–10 wpc from each salmon-virus challenge combination; n = 24 per category) or persistent (eight recorded values 14 wpc from each salmon-virus challenge combination; n = 24 per category) phases of infection. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 by one-way ANOVA and Tukey’s multiple comparisons tests of log-transformed values.</p>
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<p>Heart <span class="html-italic">cd8a, ifna</span>, and <span class="html-italic">ifng</span> transcriptional expression following PRV-1 challenge. (<b>A</b>) Corrected normalized relative quantity (CNRQ) of <span class="html-italic">ifna</span> (light circles) and <span class="html-italic">ifng</span> (dark circles) are presented relative to <span class="html-italic">cd8a</span> transcriptional expression for all (n = 384) salmon sampled between 6–12 wpc in this study. Pearson r (r) and associated <span class="html-italic">p</span>-value is provided. (<b>B</b>) Mean (line) and individual (dots) heart <span class="html-italic">cd8a</span> (left side) and <span class="html-italic">ifna</span> (right side) CNRQ transcripts in relation to histopathological cardiac inflammation score, where 0 = no inflammation (n = 201), 1 = mild inflammation (n = 151), 2 = moderate inflammation (n = 25), and 3 = severe inflammation (n = 4) for the same 6–12 wpc dataset. Letters denote groupings for mean statistical similarity by the Kruskal–Wallis nonparametric test and Dunn’s multiple comparisons tests at <span class="html-italic">p</span> &lt; 0.01. Shaded areas present one standard deviation of target transcription in cardiac inflammation score 0 fish to provide a minimum threshold suggestive of biological relevance. (<b>C</b>) Mean (line) and individual (dots) of heart <span class="html-italic">cd8a</span> transcripts measured at 6–12 wpc from PRV-1-challenged salmon (n = 288) categorized relative to PRV-1 isolate administered (n = 24 per PRV-1 isolate per time point), or (<b>D</b>) relative to Atlantic salmon strain challenged (n = 24 per salmon strain per time point). ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 by two-way ANOVA and Šídák’s multiple comparisons tests of log-transformed CNRQ values; one standard deviation of <span class="html-italic">cd8a</span> expression recorded across all SC fish (n = 96) is shaded to suggest a minimum threshold for biological relevance.</p>
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<p>Heart and red skeletal muscle inflammation following PRV-1 challenge. The cumulative prevalence (%) of heart inflammation in PRV-1-challenged salmon over 14 weeks categorized by either (<b>A</b>) the PRV-1 isolate administered or (<b>B</b>) the strain of the recipient Atlantic salmon; ** <span class="html-italic">p</span>  &lt;  0.01, *** <span class="html-italic">p</span>  &lt;  0.001 by Kruskal–Wallis and Dunn’s multiple comparisons tests. Heat maps of (<b>C</b>) heart and (<b>D</b>) red skeletal muscle presented as median inflammatory heart score in relation to each Atlantic salmon x PRV combination over a 14-week progression. Median inflammation score is defined by color, which ranges from 0 (no inflammation; white) to 3 (severe inflammation; brown). The cumulative prevalence (%) of red skeletal muscle inflammation in PRV-1-challenged salmon over the same 14 weeks categorized by either (<b>E</b>) the PRV-1 isolate administered or (<b>F</b>) the strain of recipient Atlantic salmon is also provided; *** <span class="html-italic">p</span>  &lt;  0.001 by Kruskal–Wallis and Dunn’s multiple comparisons tests.</p>
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18 pages, 6889 KiB  
Article
Machine Learning-Based Detection of Icebergs in Sea Ice and Open Water Using SAR Imagery
by Zahra Jafari, Pradeep Bobby, Ebrahim Karami and Rocky Taylor
Remote Sens. 2025, 17(4), 702; https://doi.org/10.3390/rs17040702 - 19 Feb 2025
Viewed by 405
Abstract
Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these [...] Read more.
Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article
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<p>Distribution of targets over date and location.</p>
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<p>These figures show four sample RGB images from the RCM dataset, where Red = HH, Green = HV, and Blue = (HH-HV)/2. (<b>A</b>,<b>B</b>) depict OW and SI, while (<b>C</b>,<b>D</b>) show icebergs in OW and SI. Only red circles highlight icebergs; other bright pixels represent clutter or sea ice.</p>
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<p>Block diagram illustrating the proposed system.</p>
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<p>The impact of despeckling on iceberg images in the HH channel from the SAR dataset, using mean, bilateral, and Lee filters.</p>
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<p>(<b>A</b>) shows that feature #780 exhibits the most overlap and is considered a weak feature. (<b>B</b>) In contrast, feature #114 is the strongest feature, displaying the least overlap.</p>
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<p>ROC curves for the evaluated models: (<b>A</b>) ViTFM, (<b>B</b>) StatFM, (<b>C</b>) ViTStatFM, and (<b>D</b>) ViTStatClimFM. The curves illustrate the classification performance across OW, OWT, SI, and SIT categories.</p>
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<p>Confusion matrices depicting the classification performance of the hybrid model with climate features: (<b>A</b>) represents the classification performance across all four classes, (<b>B</b>) highlights the model’s ability to distinguish between target-containing patches and those without targets, and (<b>C</b>) evaluates the classification of sea ice (SI and SIT) versus open water (OW and OWT).</p>
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<p>Application of the proposed method to a calibrated RCM image acquired on 23 June 2023. (<b>A</b>) The RCM image overlaid on the Labrador coast. (<b>B</b>) Corresponding ice chart from the Canadian Ice Service for the same region and date. (<b>C</b>) Probability map for OW. (<b>D</b>) Probability map for SI. (<b>E</b>) Probability map for OWT. (<b>F</b>) Probability map for SIT.</p>
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<p>An extracted section from the full RCM image captured on 23 June 2023, showing icebergs embedded in SI. Red triangles indicate ground truth points, while green circles represent model predictions.</p>
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<p>Missed targets located near patch borders, illustrating boundary effects. (<b>A</b>) A missed target near the top-left patch border. (<b>B</b>) A missed target within a central region affected by boundary artifacts. (<b>C</b>) A missed target near the bottom-right patch border, highlighting prediction inconsistencies at patch edges.</p>
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27 pages, 4621 KiB  
Article
Techno-Economic Modeling of Floating Wind Farms
by Ariadna Montes, David Fournely, Jens N. Sørensen and Gunner C. Larsen
Energies 2025, 18(4), 967; https://doi.org/10.3390/en18040967 - 17 Feb 2025
Viewed by 238
Abstract
A simple techno-economic model for determining wind power production and costs related to the development of floating offshore wind power is proposed. The model is a further extension of the minimalistic prediction model for fixed-bottom wind farms previously developed by two of the [...] Read more.
A simple techno-economic model for determining wind power production and costs related to the development of floating offshore wind power is proposed. The model is a further extension of the minimalistic prediction model for fixed-bottom wind farms previously developed by two of the authors. In the extended version, costs associated with the deployment of floating structures, such as floaters, mooring lines, and anchors, including additional installation and operational expenses, are taken into account. This paper gives an overview of the costs of the various components of different types of floating wind power installations, and using actual wind climate and bathymetry data for the North Sea, the model is employed to map the annual energy production and levelized cost of energy (LCoE) for floating wind farms located in the North Sea. Full article
(This article belongs to the Special Issue Wind Turbine and Wind Farm Flows)
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<p>Schematic representation of the different elements of a floating offshore wind farm.</p>
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<p>Scheme of the input parameters to the minimalistic model, allowing calculation for energy production, CAPEX, OPEX, and LCoE.</p>
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<p>Types of floating wind turbine platforms: (<b>A</b>) semi-submersible, (<b>B</b>) spar, and (<b>C</b>) tension leg platforms.</p>
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<p>Three types of mooring systems: (<b>A</b>) catenary mooring system, (<b>B</b>) tension leg mooring system, and (<b>C</b>) taut or semi-taut mooring system.</p>
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<p>Types of anchors for floating wind turbine platforms: (<b>A</b>) vertical load anchor, (<b>B</b>) drag embedment anchor, and (<b>C</b>) suction pile anchor.</p>
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<p>Breakdown of the expenditures (in percentage) for a floating offshore wind farm case.</p>
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<p>Breakdown of the costs with the influence of (<b>a</b>) water depth and (<b>b</b>) distance to shore for a floating offshore wind farm case.</p>
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<p>LCoE of a floating <b>semi-submersible</b> wind farm, with <math display="inline"><semantics> <msub> <mi>P</mi> <mi>G</mi> </msub> </semantics></math> = 15 MW, <span class="html-italic">D</span> = 245 m, <span class="html-italic">H</span> = 150 m, <span class="html-italic">S</span> = 7, <math display="inline"><semantics> <msub> <mi>N</mi> <mi>T</mi> </msub> </semantics></math> = 100, <math display="inline"><semantics> <mrow> <mi>Y</mi> <mi>O</mi> </mrow> </semantics></math> = 20 years, and <span class="html-italic">r</span> = 5%. The red isoline represents a bathymetric depth of −40 m. The red dot corresponds to the <span class="html-italic">Kincardine</span> semi-submersible wind farm.</p>
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<p>LCoE of a floating <b>spar</b> wind farm, with <math display="inline"><semantics> <msub> <mi>P</mi> <mi>G</mi> </msub> </semantics></math> = 15 MW, <span class="html-italic">D</span> = 245 m, <span class="html-italic">H</span> = 150 m, <span class="html-italic">S</span> = 7, <math display="inline"><semantics> <msub> <mi>N</mi> <mi>T</mi> </msub> </semantics></math> = 100, <math display="inline"><semantics> <msub> <mi>Y</mi> <mi>O</mi> </msub> </semantics></math> = 20 years, and <span class="html-italic">r</span> = 5%. The red isoline represents a bathymetry depth of 80 m. The red dot corresponds to the <span class="html-italic">Hywind Scotland</span> spar wind farm.</p>
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<p>Comparison of LCoE’s between a floating semi-submersible wind farm and a bottom-fixed wind farm, with <math display="inline"><semantics> <msub> <mi>P</mi> <mi>G</mi> </msub> </semantics></math> = 15 MW, <span class="html-italic">D</span> = 245 m, <span class="html-italic">H</span> = 150 m, <span class="html-italic">S</span> = 7, <math display="inline"><semantics> <msub> <mi>N</mi> <mi>T</mi> </msub> </semantics></math> = 100, <math display="inline"><semantics> <msub> <mi>Y</mi> <mi>O</mi> </msub> </semantics></math> = 20 years, and <span class="html-italic">r</span> = 5%. The red line indicates the transition from bottom-fixed to floating wind farms.</p>
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<p>Variation of the LCoE with water depth at the location of Horns Rev 2 (<b>left</b>) and Horns Rev 3 (<b>right</b>).</p>
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17 pages, 6293 KiB  
Article
Exploiting Enhanced Altimetry for Constraining Mesoscale Variability in the Nordic Seas and Arctic Ocean
by Antonio Bonaduce, Andrea Storto, Andrea Cipollone, Roshin P. Raj and Chunxue Yang
Remote Sens. 2025, 17(4), 684; https://doi.org/10.3390/rs17040684 - 17 Feb 2025
Viewed by 283
Abstract
Recent advances in Arctic observational capabilities have revealed that the Arctic Ocean is highly turbulent in all seasons and have improved temporal and spatial sampling of sea level retrievals from remote sensing, even above 80°N. Such data are expected to be increasingly valuable [...] Read more.
Recent advances in Arctic observational capabilities have revealed that the Arctic Ocean is highly turbulent in all seasons and have improved temporal and spatial sampling of sea level retrievals from remote sensing, even above 80°N. Such data are expected to be increasingly valuable in the future when the extent of sea ice in the Arctic Ocean is reduced. Assimilating this new data into ocean models, together with in situ observations, provides an enriched representation of the mesoscale population that induces new eddy-driven contributions to local dynamics and thermodynamics. To quantify the content of the new information, we compare three-year-long assimilative experiments at ¼° resolution incorporating in situ-only data, in situ and standard altimetry, and in situ and high-latitude-enhanced altimetry, respectively. The enhanced altimetry data lead to an increase in three-dimensional eddy kinetic energy, generated by coherent vortexes, of up to 20% in several areas. Robust ocean warming is generated in the Arctic sector down to 800 m. Via heat budget analysis, this warming can be ascribed to a local enhancement of vertical mixing, as well as an increase in meridional heat transport. The assimilation of enhanced altimetry amplifies the transport, compared to standard altimetry, especially north of 70°N. Full article
(This article belongs to the Special Issue Recent Advances on Oceanic Mesoscale Eddies II)
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<p><b>Top</b>: Variance of the sea-level anomaly (SLA) field (cm<sup>2</sup>) obtained considering conventional (<b>left</b>) and enhanced satellite altimetry gridded maps (L4) over the period 2017–2019. The letters in the (<b>right</b>) panel depict the areas of the Lofoten Basin (LB), Barents Sea (BS), Fram Strait (FS), Nansen Basin (NB), Kara Sea (KS), Laptev Sea (LS), Beaufort Gyre (BG), Greenland Sea (GS) and Labrador Sea (LBS). <b>Bottom</b>: SLA variance differences (cm<sup>2</sup>) between enhanced and conventional altimetry data: positive values show a larger variability in the enhanced altimetry signals.</p>
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<p>Extension and bathymetry of the CREG025 regional configuration of the NEMO model used in this study as a model component of the analysis system. The black rectangle identifies the region where the heat budget analysis was performed (<a href="#sec3-remotesensing-17-00684" class="html-sec">Section 3</a>).</p>
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<p>REKE obtained considering eddy lifetime &gt;14 days in each experiment during 2017–2019 at the surface (<b>top panels</b>). Focusing on summer months only (JJA), surface REKE is shown in the <b>central panels</b> while 3D REKE is shown in the <b>bottom panels</b>. Values are expressed as the fraction of ocean kinetic energy carried by eddies.</p>
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<p>Meridional heat transport (MHT). The panels show the MHT in the experiments, obtained considering a latitudinal range between 61°N and 82°N during 2017–2019 (<b>top left</b>), as a difference with respect to EXP0 (<b>bottom left</b>) and as a percent difference between EXP2 and EXP1 during winter (DJF), spring (MAM), summer (JJA) and autumn (SON) over the period 2017–2019.</p>
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<p>Eddy component of the MHT (<b>left panel</b>) and percent contribution of the eddy and mean components (<b>middle</b> and <b>right panels</b>) for the three experiments presented in the text, as a function of latitude.</p>
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<p>Time-averaged difference of ocean heat content (full column, <b>left panel</b>, in J m<sup>−2</sup>), sea-ice concentration (<b>middle panel</b>, dimensionless), and net downward heat flux (<b>right panel</b>, in W m<sup>−2</sup>) between experiments EXP2 and EXP1. The black rectangle identifies the region where the heat budget analysis was performed.</p>
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<p>Heat budget component analysis as a time series of different heat components for the study region (<b>left panel</b>) and vertical profiles of mean temperature, mean cumulated analysis increments, and their differences between EXP2 and EXP1 (<b>right panels</b>). OHC: Ocean heat content (total warming); NHF: net downward heat flux at the sea interface with atmosphere or ice; ANI: data assimilation analysis of increments’ contribution; TRA: lateral transport. In the rightmost panel, TEM refers to the total temperature differences, and ANI refers to those induced by the data assimilation analysis increments.</p>
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17 pages, 4493 KiB  
Article
The Effects of Climate Change on Sthenoteuthis oualaniensis Habitats in the Northern Indian Ocean
by Lihong Wen, Heng Zhang, Zhou Fang and Xinjun Chen
Animals 2025, 15(4), 573; https://doi.org/10.3390/ani15040573 - 17 Feb 2025
Viewed by 261
Abstract
The northern Indian Ocean is located in a typical monsoon region that is also influenced by climate events such as the Indian Ocean Dipole (IOD), which makes Sthenoteuthis oualaniensis habitat highly susceptible to changes in climate and marine environmental conditions. This study established [...] Read more.
The northern Indian Ocean is located in a typical monsoon region that is also influenced by climate events such as the Indian Ocean Dipole (IOD), which makes Sthenoteuthis oualaniensis habitat highly susceptible to changes in climate and marine environmental conditions. This study established a suitability index (SI) model and used the arithmetic average method to construct a comprehensive habitat suitability index (HSI) model based on S. oualaniensis production statistics in the northern Indian Ocean from 2017 to 2019. Variations in the suitability of S. oualaniensis habitat during different IOD events were then analyzed. The results indicate that the model performed best when year, month, latitude, longitude, sea surface temperature (SST), wind speed (WS), and photosynthetically active radiation (PAR) variables were included in the generalized additive model (GAM). SST, WS, and PAR were identified as the most important key environmental factors. The HSI model showed that the most suitable habitat during a positive IOD event was smaller than during a negative IOD event and that the suitable habitat’s center was located west of the positive IOD event and east of the negative IOD event. There was a significant inverse relationship between the area, suitable for habitation, and the north–south shift in the latitudinal gravity center and the Dipole modal index (DMI). The results indicate significant differences in the habitat of S. oualaniensis in the northern Indian Ocean during different IOD events, as well as differences in suitable habitat ranges and the spatial distribution of the species. Full article
(This article belongs to the Section Aquatic Animals)
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<p>The ln (CPUE + 1) frequency distribution and distribution tests for <span class="html-italic">Sthenoteuthis oualaniensis</span> in the northern Indian Ocean. (<b>A</b>) Normal Q-Q plot of ln (CPUE + 1). (<b>B</b>) Frequency distribution of ln (CPUE + 1).</p>
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<p>The distribution of HSI climatic area for <span class="html-italic">Sthenoteuthis oualaniensis</span> in the northern Indian Ocean from January to March and October to December.</p>
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<p>The distribution of <span class="html-italic">Sthenoteuthis oualaniensis</span> habitats in the northern Indian Ocean from January to March and October to December during different climatic events. nIOD stands for the negative Indian Ocean Dipole. pIOD stands for the positive Indian Ocean Dipole.</p>
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<p>Suitable HSI frequency statistics and monthly DMIs for <span class="html-italic">S. oualaniensis</span> in the northern Indian Ocean from January to March and October to December of 2000 to 2010 (<b>A</b>). Suitable HSI frequency statistics and monthly DMI variation trends for <span class="html-italic">S. oualaniensis</span> from January to March and October to December in the northern Indian Ocean from 2011 to 2020 (<b>B</b>).</p>
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<p>The relationship between HSI longitude center of gravity and annual DMI for <span class="html-italic">S. oualaniensis</span> from January to March and October to December in the northern Indian Ocean from 2000 to 2020.</p>
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<p>The relationship between HSI latitude center of gravity and annual DMI for <span class="html-italic">S. oualaniensis</span> from January to March and October to December in the northern Indian Ocean from 2000 to 2020.</p>
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