All vertebrates have a definitive thyroid gland, or thyroid for short. As a critical organ for growth, development and metabolism, its origin and evolution have long received attention. On the basis of anatomical position, endodermal origination and histological features, the endostyle of amphioxus has been proposed as a homologue to the thyroid of vertebrates. This homology is further supported by the findings that the amphioxus endostyle abounds in thyroid hormones, possesses several thyroid-specific proteins such as thyroperoxidase, nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and thyroglobulin, and expresses the thyroid-related transcription factors involved in the regulation of development of the vertebrate thyroid, including Nkx2.1, FoxE4 and Pax2/5/8. Importantly, our study on functionality, together with others, indicates significant similarities between the amphioxus endostyle and the vertebrate thyroid gland. Moreover, we show that the production of thyroid hormones by the amphioxus endostyle is mediated in a fashion similar to that of the vertebrate thyroid. These provide solid evidences that the amphioxus endostyle is the homologue of the vertebrate thyroid. From a phylogenetic viewpoint, we propose that the hypobranchial ridge, or endostyle-like structure, of hemichordates is the most primitive forerunner of the thyroid, from which the vertebrate thyroid is formed through the transformation of non-follicular endostyle of amphioxus to follicular endocrine organ of vertebrates. We also raise a couple of questions that demand further study.

Scientific and precise evaluations of the megafaunal and landform characteristics of seamounts are important guides for their protection and study. A series of manned and unmanned submersibles have provided invaluable observational imaging data for the ecological study of seamounts. However, traditional methods of artificial observation of seamount imaging data cannot accurately and efficiently determine the characteristics of megafauna and landforms. This research harnesses data-driven technology to systematically investigate the distributional traits and morphological features of megafaunal organisms, as well as the topographical characteristics, in the Caiwei Guyot region of the western Pacific’s Magellan Seamounts. To construct the landform and megafauna dataset of the Caiwei Guyot region, we used a data preprocessing technology based on image enhancement to provide high-quality imaging data for data-driven technologies. A megafaunal identification and counting algorithm based on YOLOv5 (You Only Look Once Version 5) was developed to efficiently assess the abundance, variety, and dominant species of megafauna. Simultaneously, a landform three-dimensional (3D) reconstruction algorithm based on PatchmatchNet was developed to reconstruct the 3D form of the terrain accurately. This study pioneers the application of data-driven technology to deep-sea imaging within the Caiwei Guyot region, offering an innovative approach to accurately and efficiently characterize the region’s unique megafauna and landforms.

Marine heatwave (MHW) events refer to periods of significantly elevated sea surface temperatures (SST), persisting from days to months, with significant impacts on marine ecosystems, including increased mortality among marine life and coral bleaching. Forecasting MHW events are crucial to mitigate their harmful effects. This study presents a two-step forecasting process: short-term SST prediction followed by MHW event detection based on the forecasted SST. Firstly, we developed the “SST-MHW-DL” model using the ConvLSTM architecture, which incorporates an attention mechanism to enhance both SST forecasting and MHW event detection. The model utilizes SST data from the preceding 60 d to forecast SST and detect MHW events for the subsequent 15 d. Verification results for SST forecasting demonstrate a root mean square error (RMSE) of 0.64℃, a mean absolute percentage error (MAPE) of 2.05%, and a coefficient of determination (R²) of 0.85, indicating the model’s ability to accurately predict future temperatures by leveraging historical sea temperature information. For MHW event detection using forecasted SST, the evaluation metrics of “accuracy”, “precision”, and “recall” achieved values of 0.77, 0.73, and 0.43, respectively, demonstrating the model’s capability to capture the occurrence of MHW events accurately. Furthermore, the attention-enhanced mechanism reveals that recent SST variations within the past 10 days have the most significant impact on forecasting accuracy, while variations in deep-sea regions and along the Taiwan Strait significantly contribute to the model’s efficacy in capturing spatial characteristics. Additionally, the proposed model and temporal mechanism were applied to detect MHWs in the Atlantic Ocean. By inputting 30 d of SST data, the model predicted SST with an RMSE of 1.02℃ and an R² of 0.94. The accuracy, precision, and recall for MHW detection were 0.79, 0.78, and 0.62, respectively, further demonstrating the model’s robustness and usability.

As the most important component of marine siliceous organisms, diatoms are vital primary producers of the ocean that are often used as indicators of paleoenvironmental change. To understand the response of sedimental diatoms to regional environmental changes and the factors affecting the distribution of sedimental diatoms in the Taiwan Strait, this study quantified and classified the diatoms found in surface sediments collected during four surveys from 2019 to 2020. Overall, 118 diatom taxa and 44 genera were identified with total diatom abundance of 8–27 353 valves/g. Four diatom assemblages representing different environments were identified. Among them, assemblage Ⅰ represented a coastal environment, assemblage Ⅱ comprised warm water species of a coastal environment, Assemblage Ⅲ represented a coastal environment affected markedly by exorheism, Assemblage Ⅳ represented a group with lowest diatom abundance. Seasonal variation in total diatom abundance was controlled by seven environmental factors: depth, sea surface salinity, mean grain size, silicate, nitrite, nitrate, and phosphate. Spatiotemporal variation in each of the diatom assemblages was substantial and strongly affected by various currents, upwelling, and low-salinity water. Specifically, it was found that the succession of diatom assemblages reflects change in the range of influence of local warm currents.

Estuaries are often a significant source of atmospheric CO₂. However, studies of carbonate systems have predominantly focused on large estuaries, while smaller estuaries have scarcely been documented. In this study, we collected surface and bottom seawater carbonate samples in the subtropical Jiulong River Estuary across different tidal levels from 2019 to 2021. The results showed that estuarine mixing of freshwater from the river with seawater was the dominant factor influencing the estuarine carbonate system. Moreover, estuarine mixing is concomitantly impacted by the net metabolism of biological production and decomposition, groundwater input, release of CO₂ from the estuary, and precipitation or dissolution of calcium carbonate. The estuarine partial pressure of CO₂ (pCO₂) varied from 530 μatm to 7715 μatm, which represents a strong source of atmospheric CO₂. The mean annual air-sea CO₂ flux estimated from three different parameterized equations was approximately (25.63 ± 10.25) mol/(m²·a). Furthermore, the annual emission to the atmosphere was approximately (0.031 ± 0.012) Tg C, which accounts for a mere 0.0077%−0.015% of global estuarine emissions. Dissolved inorganic carbon (DIC), total alkalinity (TA) and the pCO₂ exhibited high variability throughout the tidal cycle across all cruises. Specifically, the disparities observed between DIC and TA during low and high tides at identical stations during all cruises ranged from approximately 15% to 30%. The variance in the pCO₂ was even more pronounced, ranging from approximately 30% to 40%. Thus, tidal discrepancies may need to be taken into consideration to estimate the CO₂ flux from estuarine systems more accurately.

The Macao Special Administrative Region is located in the southeastern coastal area of China. The region of Macao was narrow in the history, so land reclamation has become a main means of expanding its geographical scope. Exploring the significance of land reclamation for the planning and urban construction of the Macao region provides valuable references. (1) The Google Earth Engine (GEE) cloud processing platform is used in this study to calculate the modified normalized difference water index (MNDWI) based on Landsat data from 1986 to 2021; (2) the Jenks natural index classification method is used to extract the water body range, and the water body boundary as well as area at different periods is calculated combined with the neural network classification method in the environment for visualizing images (ENVI) software; (3) this study then combines the patch-generating land use simulation (PLUS) model to predict the future trend of shoreline changes in the study area in 2036. The result indicates that the MNDWI and neural net classification method lead to a high classification accuracy with both the overall accuracy (OA) and Kappa coefficient being higher than 87%. Land reclamation activities in Macao were gradually intense from 1986 to 2021, with social and economic conditions such as transportation being main influencing factors, which provides valuable references and inspiration for the regional planning of the Macao Special Administrative Region.

Accurate significant wave height (SWH) prediction is essential for the development and utilization of wave energy. Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting. However, these methods do not adapt well to dynamic seasonal variations in wave data. In this study, we propose a novel method—the spatiotemporal dynamic graph (STDG) neural network. This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion. First, considering the dynamic seasonal variations in the wave direction over time, the network models wave dynamic spatial dependencies from long- and short-term pattern perspectives. Second, to correlate multiple characteristics with SWH, the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics. Finally, we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories. The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction. Furthermore, an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

We introduce a new method, the piecewise Reynolds mean (PREM), for decomposing the flow velocity into the mean-flow and eddy-flow parts in the time domain for subsequent calculation of the mean flow kinetic energy (MKE) and eddy kinetic energy (EKE). Compared with conventional methods like the Reynolds mean and running mean (RUM), PREM has the advantage of exact balance between the MKE and EKE, without the additional residual kinetic energy (RKE), while retaining time-dependent mean-flow. It is mathematically simple and computationally lightweight, depending on a pre-defined separation scale for the mean-flow and eddies. Based on satellite observations and the separation scale of 1 year, we compare PREM with RUM, as well as another newly proposed method, the eddy detection and extraction (EDEX). The latter is based on objective identification of mesoscale eddies and eddy anomaly extraction algorithms, and is therefore only suitable for mesoscale eddy energetics, but independent of separation scales. It is shown that compared with RUM, PREM gives larger mean EKE and stronger interannual variability. In strong-current and eddy-rich regions, the two methods differ the most (max: Kuroshio Extension, root-mean-sqaure-difference = 60.3 J/m³); but in areas with weak current and eddy, the difference accounts for the largest fraction of total EKE (max: south of the Aleutian Islands, 208%). EKE estimated by the two methods is out of phase (min correlation coefficient = 0.38). The mean EKE and standard deviation from the EDEX method resemble the PREM with 1-year separation scale, but is generally smaller in magnitude.

As our understanding of ecology deepens and modeling techniques advance, species distribution models have grown increasingly sophisticated, enhancing both their fitting and predictive capabilities. However, the dependability of predictive accuracy remains a critical issue, as the precision of these predictions largely hinges on the quality of the base data. We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions. Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data. Within the same season, we found that the relationship between the abundance of S. japonicus and environmental factors varied significantly depending on the data source. Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors. Additionally, in terms of model predictive performance, models based on field survey data demonstrated greater accuracy in predicting the abundance of S. japonicus compared to those based on remote sensing data, allowing for more accurate mastery of their spatial distribution characteristics. This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.

By establishing a distribution and environmental factor database of 21 typical harmful dinoflagellates in global waters, the MaxEnt model was used to predict shifts in the habitat of harmful dinoflagellates in Chinese waters under global climate change. The results revealed that offshore distance was the most important predictive factor and that surface seawater temperature (SST), primary productivity, and nitrate concentration were the key ecological factors influencing the distribution of harmful dinoflagellates. Under the low greenhouse gas emission scenario defined by the Intergovernmental Panel on Climate Change (IPCC), by approximately 2050, 17 of the 21 harmful dinoflagellate species in high-suitability areas (HSA) will migrate northward, six species will migrate eastward, and six species will expand their HSA. By 2100, approximately 18 of the 21 harmful dinoflagellate species in HSA will have migrated northward, seven species will have migrated eastward, and four species will have expanded their HSA. Notably, the HSA content of highly toxic Alexandrium minutum is expected to increase by 13.4% and 9.4% by 2050 and 2100, respectively. Under the high greenhouse gas emissions, there will be 17 species migrating northward, 6 species migrating eastward, and 4 species increasing in their size in HSA by 2050; moreover, there will be 16 species migrating northward, 2 migrating eastward, and 4 species according to their size of HSA by 2100. Specifically, the HSA of A. minutum is predicted to increase by 7.0% and 25.9% by 2050 and 2100, respectively. Notably, A. ostenfeldii, which is currently seldom present in the China seas, is predicted to exhibit an HSA in most coastal areas of the Yellow Sea, the Bohai Sea, the Hangzhou Bay, the Zhejiang Coast, and the Beibu Gulf of the South China Sea. Conversely, the HSA of Noctiluca scintillans, a typical red-tide species, will be reduced by 7%–90%. The northward migration of Karenia mikimotoi exceeded 100 km and 300 km under low and high greenhouse gas emission scenarios, respectively. These changes underscore the significant impact of climate change on the distribution and habitat suitability of harmful dinoflagellates, thus indicating a potential shift in their ecological dynamics and consequent effects on marine ecosystems.