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Surface and sub-surface ocean temperature observations collected by sea turtles (ST) during the first phase (Jan 2019-April 2020) of the Sea Turtle for Ocean Research and Monitoring (STORM) project are compared against in-situ and satellite temperature measurements, and later relied upon to assess the performance of the Glo12 operational ocean model over the west tropical Indian Ocean. The evaluation of temperature profiles collected by STs against collocated ARGO drifter measurements show good agreement at all sample depths (0-250 m). Comparisons against various operational satellite sea surface temperature (SST) products indicate a slight overestimation of ST-borne temperature observations of ∼0.1°±°0.6° that is nevertheless consistent with expected uncertainties on satellite-derived SST data. Comparisons of ST-borne surface and subsurface temperature observations against Glo12 temperature forecasts demonstrate the good performance of the model surface and subsurface (50 m), the model is, however, shown to significantly underestimate ocean temperatures as already noticed from global evaluation scores performed operationally at the basin scale. The distribution of model errors also shows significant spatial and temporal variability in the first 50 m of the ocean, which will be further investigated in the next phases of the STORM project.

Method based on field data performs better than traditional management forecast tools.

Method based on field data performs better than traditional management forecast tools.

Tropical shallow-water habitats represent the marine environments with the greatest biodiversity; however, these habitats are the most vulnerable to climate warming. Corals, seagrasses, and macroalgae play a crucial role in the structure, functions, and processes of the coastal ecosystems. Understanding their growth and physiological responses to elevated temperature and interspecific sensitivity is a necessary step to predict the fate of future coastal community. Six species representatives, including Pocillopora acuta, Porites lutea, Halophila ovalis, Thalassia hemprichii, Padina boryana, and Ulva intestinalis, collected from Phuket, Thailand, were subjected to stress manipulation for 5 days. Corals were tested at 27, 29.5, 32, and 34.5°C, while seagrasses and macroalgae were tested at 27, 32, 37, and 42°C. After the stress period, the species were allowed to recover for 5 days at 27°C for corals and 32°C for seagrasses and macroalgae. Non-destructive evaluation of photosynthetic parameters (Fv/Fm, Fv/F0, ϕPSII and rapid light curves) was carried out on days 0, 3, 5, 6, 8, and 10. Chlorophyll contents and growth rates were quantified at the end of stress, and recovery periods. An integrated biomarker response (IBR) approach was adopted to integrate the candidate responses (Fv/Fm, chlorophyll content, and growth rate) and quantify the overall temperature effects. Elevated temperatures were found to affect photosynthesis, chlorophyll content, and growth rates of all species. Lethal effects were detected at 34.5°C in corals, whereas adverse but recoverable effects were detected at 32°C. Seagrasses and macroalgae displayed a rapid decline in photosynthesis and lethal effects at 42°C. In some species, sublethal stress manifested as slower growth and lower chlorophyll content at 37°C, while photosynthesis remained unaffected. Among all, T. hemprichii displayed the highest thermotolerance. IBR provided evidence that elevated temperature affected the overall perf

Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale

Accurately estimating the ocean's subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model's accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.

Haslea ostrearia represents the model species of blue diatoms, a cluster of benthic marine species all belonging to the genus Haslea, noticeable for producing a blue pigment called marennine famous for its greening activity on the gills of bivalves but also for its potential in biotechnology. The exact distribution of H. ostrearia is unknown. It has been long considered a cosmopolitan diatom, but recent studies provided evidence for cryptic diversity and the existence of several other blue species, some of them inhabiting places where diatoms described as H. ostrearia had previously been observed. Recently, a marine diatom with blue tips was isolated into clonal culture from a plankton net sample from Kei Mouth on the Indian Ocean coast of South Africa. It was identified as H. ostrearia through a combination of LM/SEM microscopy and molecular analysis. This constitutes the first established record of this species from South Africa and the Indian Ocean and the second record for the southern hemisphere. Molecular barcoding clearly discriminated the South African strain from an Australian strain and cox1 based molecular phylogeny associated it instead with strains from the French Atlantic Coast, raising questions about the dispersal of this species. The complete mitochondrial and plastid genomes were compared to those of Haslea nusantara and Haslea silbo. Multigene phylogenies performed with all protein-coding genes of the plastome and the mitogenome associated H. ostrearia with H. silbo. In addition, complete sequences of circular plasmids were obtained and one of them showed an important conservation with a plasmid found in H. silbo.

Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling - regarded as the largest in the WIO and the fifth most important upwelling system globally - takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

Monthly composites of remote sensing reflectance at 555 nm wavelength (Rrs555) from ocean color imagery of the MODIS sensor onboard the Aqua platform were used to characterize the spatial and temporal variability of coastal plume in the Sofala Bank and its relation to river discharge, local rainfall, and wind speed. To achieve the objective, maps of monthly composites of Rrs555 over the Sofala Bank were inspected and statistical analysis was performed, including correlation, analysis of variance, and wavelet coherence between environmental variables and both plume area and Rrs555. Climatology of Rrs555 revealed that both plume dispersion and Rrs555 values are higher during June to December and lower during January to May. A positive correlation (r = 0.77) between wind speed and monthly time series of Rrs555, and a negative correlation between the Zambezi river discharge (r = −0.21) and rainfall (r = −0.67) with Rrs555 were found. These results suggest that variation of suspended matter in the Sofala Bank is mainly controlled by erosion and re-suspension by winds rather than the input of terrigenous matter by the Zambezi River discharge and rainfall, assuming that Rrs555 can be a valid proxy for the inorganic suspended matter. The southern portion of the Sofala Bank (i.e., near the mouths of the Pungue and Buzi Rivers) presented higher values of Rrs555 if compared to the center region near Zambezi river mouth and the northern region near Licungo river mouth. The higher Rrs555 values in the southern region might be associated with higher re-suspension rates due to increased tide mixing, dredging activities, and the shallower nature of bathymetry in the southern region. The dominance of wind in controlling the variability of suspended sediments and the eventual relatively greater contribution of Pungue and Buzi River than the Zambezi in supplying sediments could represent an evidence of weakening of Zambezi River supply of sediments, a process that might have start

The groupers (Epinephelidae family) are demersal species that are a vulnerable resource due to increasing fishing pressure around Reunion Island. Five species of groupers are among the main species exploited by commercial and recreational fisheries around La Réunion Island: blacktip grouper (Epinephelus fasciatus; Forsskål 1775), oblique-banded grouper (Epinephelus radiatus; Day 1868), golden hind (Cephalopholis aurantia, Valenciennes 1828), white-edged lyretail (Variola albimarginata; Baissac 1953) and yellow-edged lyretail (Variola louti; Fabricius 1775). From 2014 to 2021, a total of 482 individuals were caught. Body length-weight relationships showed a significant relationship between total length and total weight for all species. Among the five grouper species, significant sexual dimorphism was only observed for E. fasciatus. For each grouper species, the von Bertalanffy model gave the best fit for the ageing data. While the unconstrained von Bertalanffy model fitted very well to the data of four species (C. aurantia, E. radiatus; V. albimarginata and V. louti), the Gompertz model gave the best fit for the ageing data of E. fasciatus. The parameters of these growth models gave the asymptotic length TL∞ (from 28.9 cm for C. aurantia to 76.6 cm for V. louti), and growth rate K (from 0.16 for V. albimarginata to 0.40 for E. fasciatus) for each species. Consequently the growth performance index for these grouper species varied from 2.40 to 3.09. Based on gonad observation, the length at first sexual maturity of females varied between 14 to 18 cm for C. aurantia, E. fasciatus and V. albimarginata, to 32 cm for E. radiatus and 34 cm for V. louti. The corresponding age at first sexual maturity by species ranged from 1.67 to 6.65 years old. Reproduction intensity showed that reproduction peaked for a period of three months each year. Three species (C. aurantia; E. fasciatus and V. louti) reproduced mainly in summer, between December to March, while E. radiatus and

Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth's climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980-2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation.

The Java Trench is the only subduction trench in the Indian Ocean that extends to the hadal zone (> 6,000 m water depth), and except for sevenbenthic trawls acquired around the 1950s, there has been little to no sampling at hadal depths undertaken since. In 2019, we undertook a 5-day expedition comprising a scientific dive using a full ocean depth-rated submersible, the DSV Limiting Factor, seven hadal-lander deployments, and high-resolution bathymetric survey. The submersible performed a video transect from the deepest point of the trench, up a 150 m high near-vertical escarpment located on the forearc, and then across a plateau at a depth of ∼7,050 m to make in situ observations of the habitat heterogeneity and biodiversity inhabiting these hadal depths. We found the Java Trench hadal community to be diverse and represented by 10 phyla, 21 classes, 34 orders and 55 families, with many new records and extensions in either depth or geographic range, including a rare encounter of a hadal ascidian. The submersible transect revealed six habitats spanning the terrain. The deepest trench axis comprised fine-grained sediments dominated by holothurians, whereas evidence of active rock slope failure and associated talus deposits were prevalent in near-vertical and vertical sections of the escarpment. Sediment pockets and sediment pouring down the steep wall in "chutes" were commonly observed. The slope terrain was dominated by two species in the order Actiniaria and an asteroid, as well as 36 instances of orange, yellow, and white bacterial mats, likely exploiting discontinuities in the exposed bedrock, that may indicate a prevalence of chemosynthetic input into this hadal ecosystem. Near the top of the escarpment was an overhang populated by > 100 hexactinellid (glass) sponges. The substrate of the plateau returned to fine-grained sediment, but with a decreased density and diversity of epifauna relative to the trench floor. By providing the first visual insights of the h

Naval coalition preforming drug busts at unprecedented rate in the Middle East.