Dynamic Marine Environment Research Articles

Exploring Technological Innovation in Wave Forecasting Using Machine Learning: A Literature Analysis

In the face of rapid technological advancements, innovations in wave forecasting are increasingly essential for effectively addressing the complex impacts of climate change. This study aims to explore technological developments in wave forecasting that can manage the complexities related to climate change and enhance the accuracy and efficiency of predictions in dynamic marine environments. Employing a qualitative approach through a Systematic Literature Review (SLR) methodology, the research focuses on literature from databases such as Scopus, DOAJ, and Google Scholar, specifically targeting publications from 2014 to 2024. Recent findings reveal that advancements in machine learning technologies, including deep learning, ensemble learning, transfer learning, and data augmentation, have significantly improved the precision and efficiency of wave forecasting models. Techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been particularly effective in capturing complex, non-linear patterns within wave data, enhancing the overall prediction accuracy. Ensemble learning methods have further contributed by increasing the stability and robustness of forecasts. Moreover, transfer learning and data augmentation play vital roles in adapting these models to rapidly changing environmental conditions, making them highly relevant in the context of climate change. These approaches are crucial for models to remain adaptable and responsive to dynamic oceanic conditions influenced by climate variability. The insights derived from this study are expected to provide valuable direction for the future development of machine learning-based wave forecasting models, emphasizing the need for innovative techniques that can accommodate the complexities and uncertainties brought about by climate change.

Unraveling the Complexity of the N e/N c Ratio for Conservation of Large and Widespread Pelagic Fish Species: Current Status and Challenges.

Estimating and understanding the ratio between effective population size (N e) and census population size (N c) are pivotal in the conservation of large marine pelagic fish species, including bony fish such as tunas and cartilaginous fish such as sharks, given the challenges associated with obtaining accurate estimates of their abundance. The difficulties inherent in capturing and monitoring these species in vast and dynamic marine environments often make direct estimation of their population size challenging. By focusing on N e, it is conceivable in certain cases to approximate census size once the N e/N c ratio is known, although this ratio can vary and does not always increase linearly, as it is influenced by various ecological and evolutionary factors. Thus, this ratio presents challenges and complexities in the context of pelagic species conservation. To delve deeper into these challenges, firstly, we recall the diverse types of effective population sizes, including contemporary and historical sizes, and their implications in conservation biology. Secondly, we outline current knowledge about the influence of life history traits on the N e/N c ratio in the light of examples drawn from large and abundant pelagic fish species. Despite efforts to document an increasing number of marine species using recent technologies and statistical methods, establishing general rules to predict N e/N c remains elusive, necessitating further research and investment. Finally, we recall statistical challenges in relating N e and N c emphasizing the necessity of aligning temporal and spatial scales. This last part discusses the roles of generation and reproductive cycle effective population sizes to predict genetic erosion and guiding management strategies. Collectively, these sections underscore the multifaceted nature of effective population size estimation, crucial for preserving genetic diversity and ensuring the long-term viability of populations. By navigating statistical and theoretical complexities, and addressing methodological challenges, scientists should be able to advance our understanding of the N e/N c ratio.

Multispectral Insights into Turbidity Variations Over Time in The Derawan Island

Abstract This investigation delves into the temporal and spatial dynamics of turbidity in the Derawan Islands, Indonesia, utilizing a comprehensive approach that combines multispectral satellite imagery from Sentinel-2, in-situ measurements via AAQ Licor sensors across 165 observation stations, and continuous data collection from a Turbidity Logger. Situated within the Coral Triangle, understanding the turbidity variations in the Derawan Islands is crucial for the conservation and sustainable management of its marine ecosystems. Our research developed and validated empirically derived algorithms to accurately estimate turbidity, utilizing a strategic partition of in-situ data—70% for model development and 30% for validation. This approach resulted in robust models, demonstrating their efficacy with Root Mean Square Error (RMSE) values as low as 0.85 and R-Squared (R2) values up to 0.56, indicating a high degree of accuracy in satellite-derived turbidity measurements. The study unveiled significant spatial and temporal turbidity heterogeneity, linking these variations to both natural and anthropogenic factors. The high-resolution data from the Turbidity Logger revealed critical diurnal fluctuations and short-term turbidity events, providing insights into the dynamic marine environment of the Derawan Islands.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Multi‐year monitoring shows higher species richness and diversity of fish assemblages in a Danish seagrass meadow as compared to neighbouring non‐vegetated areas

AbstractSeagrass meadows provide an important nursery and feeding habitat for fish, globally. However, limited data exist on how these vegetated coastal ecosystems affect local fish stocks over longer time periods. By means of beach seine hauling with a bio‐monitoring seine net, we collected fish data in Kronborg Bay (Denmark) over 4 years. The bay contains both vegetated and bare sediment areas in close proximity to Kronborg Castle in Elsinore and is part of the Øresund strait; a dynamic marine environment linking the Baltic Sea with the inner Danish waters (Kattegat). We investigated the biodiversity and fish abundance in a healthy seagrass meadow and compared it with a bare adjacent sediment area. We show that seagrass is important for fish species like the Atlantic cod, the two‐spotted goby, and the broadnosed pipefish. The seagrass meadow harboured more fish species and higher biodiversity, while the number of individuals was higher in the adjacent bare sediment area as a result of high abundances of lesser sand eel. Pilou's evenness and the Shannon‐Wiener index showed 2–4‐fold higher biodiversity in the seagrass meadow. The seagrass meadow harboured about 35% more fish species than the bare adjacent sediment. The Atlantic cod was almost entirely found in the seagrass meadow, while lesser sand eel that showed an overall increase in abundance in both habitats, represented the largest proportion of the total number of fish individuals (up to about 60%) and was mostly found on the bare adjacent sediment. Species abundance was analysed for changes over time, where, for example the European plaice showed an increase in abundance over the 4‐year period of investigation. Seagrass meadows can thus be very important for the Atlantic cod population in the Øresund strait and generally for local fish productivity, abundance and diversity.

Deep reinforcement learning for artificial upwelling energy management

The potential of artificial upwelling in stimulating seaweed growth, consequently enhancing ocean carbon sequestration has been gaining increasing attention in recent years. This has led to the development of the first solar-powered and air-lifted artificial upwelling system (AUS) in China. However, effective scheduling of the air injection system and energy storage system in dynamic marine environments remains a crucial challenge in the operation of the AUS, as it holds the potential to significantly improve system performance. To tackle this challenge, we propose a novel energy management approach that utilizes the deep reinforcement learning (DRL) algorithm to determine the optimal operational parameters of the AUS at each time interval. Specifically, we formulate the energy optimization problem as a Markov decision process and integrate the quantile network in distributional reinforcement learning with the deep dueling network to solve the problem. Through extensive simulations, we evaluate the performance of our algorithm and demonstrate its superior effectiveness over traditional rule-based approaches and other DRL algorithms in enhancing energy utilization while ensuring the secure and reliable operation of the AUS. Our findings suggest that a DRL-based approach offers a promising way to provide valuable guides for the operation of the AUS and enhance the sustainability of seaweed cultivation and carbon sequestration in the ocean.

Harnessing eDNA metabarcoding to investigate fish community composition and its seasonal changes in the Oslo fjord

In the face of global ecosystem changes driven by anthropogenic activities, effective biomonitoring strategies are crucial for mitigating impacts on vulnerable aquatic habitats. Time series analysis underscores a great significance in understanding the dynamic nature of marine ecosystems, especially amidst climate change disrupting established seasonal patterns. Focusing on Norway's Oslo fjord, our research utilises eDNA-based monitoring for temporal analysis of aquatic biodiversity during a one year period, with bi-monthly sampling along a transect. To increase the robustness of the study, a taxonomic assignment comparing BLAST+ and SINTAX approaches was done. Utilising MiFish and Elas02 primer sets, our study detected 63 unique fish species, including several commercially important species. Our findings reveal a substantial increase in read abundance during specific migratory cycles, highlighting the efficacy of eDNA metabarcoding for fish composition characterization. Seasonal dynamics for certain species exhibit clear patterns, emphasising the method's utility in unravelling ecological complexities. eDNA metabarcoding emerges as a cost-effective tool with considerable potential for fish community monitoring for conservation purposes in dynamic marine environments like the Oslo fjord, contributing valuable insights for informed management strategies.

Geochemical behavior of C, N, and S in sediments of Hangzhou Bay, Southeastern China: implications for the study of paleoclimate and sea-level changes

The correlation between the amount of organic carbon (OC) and sulfur (S) in sediments has been widely used as a paleosalinity indicator to distinguish between marine and freshwater environments. However, whether the ratio of total OC to total S (TOC/TS) can be used to identify unsteady or dynamic marine environments across sedimentary strata is still contended. An HZW1907 sediment core of 80 m in length was successfully collected in the middle of Hangzhou Bay (HZB), serving as one of the few boreholes that are crucial for the study of geologic and geo-environment changes in the coastal regions of eastern China since the Last Glacial Maximum (LGM). Total OC (TOC), stable carbon isotope, and TS of 82 subsamples from the HZW1907 core were analyzed to reconstruct the history of the shallow water biological pump and sulfur preservation record in the bay since the Late Pleistocene. Our results indicate that the samples had low concentrations of TOC (0.21%) and total nitrogen (TN) (0.02%), high mass ratio of TOC/TN (10.8), low δ13C (−24.9‰), low TS content (0.06%), and a high ratio of TOC/TS (9.1) from 33.6 ka BP to 12.3 ka BP, implying that freshwater organic matter (OM), algae, and C3 plant fragments were the main sources of OM in a relatively cold environment. The abundances of TOC, TN, and TS increased to 0.56%, 0.07%, and 0.4%, respectively, while δ13C (−23.9‰) increased and TOC/TS (2.7) decreased in the Holocene sediments, suggesting that seawater began to influence the composition of the sediments of HZB. Climate warming, which is likely to have impacted the results, was experienced from 12.3 ka BP. An OC isotope mixing model indicated that since the Mid-late Holocene, more than 70% of riverine OM accounted for the total OM. The TOC/TS ratio was identified as an effective indicator of seawater intrusion, with C/S ratios of 1–6 being considered to indicate a “sea–land transitional zone” sedimentary environment, a C/S >6 indicating freshwater, and a C/S<1 indicating normal marine facies. These findings provide crucial evidence for using TOC/TS to distinguish freshwater from marine environments and enhance our understanding of past climate changes. Therefore, these geochemical indicators can be used in conjunction with other sedimentary records to obtain accurate results about sedimentary evolution.

Object Manipulation in Marine Environments using Reinforcement Learning

Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code.

Model-free adaptive control of average coolant temperature in a novel marine small lead–bismuth-cooled reactor

Marine lead–bismuth-cooled nuclear reactors are sensitive to the marine environment, facing motion-induced changes like heeling and rolling. These changes induce heavy oscillatory processes, and general controllers cannot totally meet the power output requirements from reactors. To address these challenges, this paper proposes a model-free adaptive control (MFAC) method. Initially, an initial model is established based on mechanism analysis, incorporating neutron kinetics, fuel thermal dynamics, and core thermal dynamics of the reactor while considering a dynamic marine environment. Subsequently, a model-free adaptive controller with online identification and adaptive capability is designed, employing a projection algorithm to continuously identify initial model parameters to match the harsh marine condition and correct the control law based on the input and output data. Finally, through simulation experiments, the effectiveness of the MFAC controller is demonstrated, showcasing superior adaptive capabilities and robustness, enabling rapid and precise tracking of the desired setpoint for average coolant temperature, even under fluctuating marine conditions.

Modeling seasonal changes in the habitat suitability of Coilia nasus in the Yangtze River Estuary using tree-based methods

Estuaries, as highly dynamic marine environments, exhibit seasonal changes in fish habitat requirements, and the selection of appropriate analytical tools is critical to accurately reveal and understand such changes. Tree-based methods (including classification and regression tree (CART), random forest (RF), and conditional random forest (CRF)) have been widely used to explore the habitat requirements and predict the spatial distribution of marine species. However, the optimal methods still need to be comparatively evaluated based on the specific target species and region. This study focused on the important fishery resource Coilia nasus in the Yangtze River Estuary. Based on trawl monitoring survey data from the end of 2013–2018, seven explanatory variables, namely temperature, total nitrogen, latitude, year, salinity, pH, and ammonium, were used as predictors. The area under the receiver operating characteristic curve (AUC), Kappa, true skill statistic (TSS), and three other indicators were used to evaluate the overall performance of the three tree method models. The results showed that (1) almost all indicators pointed to the RF model having the best performance, while CRF and CART were significantly worse for some indicators, and the RF model had the best robustness and was suitable for habitat modeling in all seasons; (2) temperature and total nitrogen constituted the key habitat requirements of C. nasus, and habitat suitability rapidly increased or peaked in the temperature and total nitrogen ranges of 18.1–22.6 °C and 0–0.5 mg/L, respectively; (3) the spatial characteristics of C. nasus habitat in the Yangtze River Estuary exhibited seasonal changes in that the suitable area tended to expand gradually from spring to winter, and the northern branch of the Yangtze River and the offshore area of the Yangtze River were the main areas with high suitability for C. nasus. This study provides a useful reference for the spatial planning of the Yangtze River Estuary fishing ban area and the formulation of related conservation measures in the future.

A novel quantitative analysis for diurnal dynamics of Ulva prolifera patch in the Yellow Sea from Geostationary Ocean Color Imager observation

IntroductionIn the last decade, the outbreak of large-scale green tides caused by Ulva prolifera has continuously occurred in the Yellow Sea. Satellite remote sensing techniques have been widely used to monitor the distribution area and duration of green tides due to their advantages of their large-area synchronous observation. Ulva prolifera in the Yellow Sea is mainly distributed in bands or large patches during its flourishing stage. Previous studies have rarely reported the quantitative analysis of a single Ulva prolifera patch and its changes in the short term.MethodsConsidering the high temporal resolution of the Geostationary Ocean Color Imager (GOCI) sensor and the patchy distribution of Ulva prolifera floating on the sea surface, we developed a feasible method for monitoringUlva prolifera by performing clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) to capture the diurnal variation characteristics of a single Ulva prolifera patch.ResultsThis new approach was used to extract informationfrom a single Ulva prolifera patch in the Yellow Sea in 2012 and 2017. The results showed that during the time of GOCI imaging, the tidal current was the main factor driving the drift of Ulva prolifera, and the drifting direction of Ulva prolifera was consistent with the direction of the local tidal current, with a coefficient of determination of 0.94.DiscussionBy changing the normalized difference vegetation index (NDVI) threshold, further more accurate atmospheric correction (AC) of GOCI data during the twilight periods was indirectly achieved. By comparing the areal change in the single patch before and after AC, we speculated that the daily change in signal intensity received by the GOCI sensor may be the main reason for the diurnal variation in the Ulva proliferacoverage area. The results showed the details of the diurnal variation in Ulvaprolifera patches in the dynamic marine environment, and the main reason that may cause this variation was speculated.

A Periodically Updated Adaptive Sampling Framework for Marine Mobile Observation Platforms

Abstract While numerical models have been developed for several years, some of these have been applied to ocean-state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking a coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms. Significance Statement The ocean is very difficult to observe because it is so vast. How to deploy observing assets is a question worth investigating. We designed an adaptive sampling framework for mobile observing platforms to improve the prediction accuracy of numerical models. This framework uses the prediction to design the observation scheme, then the observation to update the prediction, and then the updated prediction to adjust the sampling scheme, which achieves closed-loop optimization. We first selected an optimization algorithm for this framework by comparing experiments. Based on the optimization algorithm, we simulated the closed-loop optimization process. Simulation results show that this framework can significantly increase the efficiency of long-term ocean observations with mobile devices.

Cooperative strategy for pursuit-evasion problem in the presence of static and dynamic obstacles

In complex and dynamic marine environments with multiple obstacles, efficient and flexible pursuit is a challenging task. This paper proposes a self-organizing cooperative pursuit strategy. First, the Apollonius circle is introduced as the final formation of the encirclement pattern, and the trajectory of the target is predicted on the basis of the least-squares method. To improve the efficiency of collision avoidance against static irregular obstacles and obtain a more reasonable collision avoidance path, we design a discretization method to describe the boundary of obstacles and combine it with the artificial potential field (APF) method. Furthermore, dynamic obstacle avoidance is considered to extend the applicability of the algorithm to a variety of obstacle environments. Compared with the common greedy algorithm, the simulation results show that in the obstacle free and static obstacle environment, the self-organizing cooperative pursuit algorithm can shorten the pursuit time and improve the movement consistency of the USVs swarm, and the obstacle avoidance route is smoother. In complex environments containing static obstacles and dynamic ships, the self-organizing cooperative pursuit algorithm can also pursuit escape targets effectively.

Revised chlorophyll-a algorithms for satellite ocean color sensors in the East/Japan Sea

The East/Japan Sea (hereafter, East Sea (ES)) is a semi-enclosed marginal sea and considered as a miniature ocean with oceanic features (e.g., currents, eddies, fronts, upwelling) and dynamic marine environments. The ES is significantly influenced by the global ocean warming with fast increases in sea surface temperature during the last several decades. The climate-induced environmental changes can affect marine ecosystem and food webs in the ES. Therefore, more accurate estimates in phytoplankton biomass in the ES with high spatial and temporal resolutions are necessary to investigate phytoplankton production, fisheries, and carbon budget as well as biological responses to the environmental and climate changes. The NASA ocean chlorophyll-type (OCx) chlorophyll-a (Chl-a) algorithms based on blue to green band ratio for the satellite ocean color data in the global ocean have been evaluated using the in situ radiometric and Chl-a measurements in the ES. The model-derived Chl-a data using the OC Chl-a algorithms present systematic overestimation in the lower Chl-a concentrations between about 0.1 and 1.0 mg/m3 in the ES (with ∼40% uncertainties). New Chl-a algorithms for the ES has been derived from the 3rd polynomial regression fits of in situ Chl-a and maximum band ratios of remote sensing reflectance (Rrs). The revised Chl-a algorithms considerably improve the Chl-a data in the ES from the satellite ocean color data in the lower Chl-a concentrations. Thus, the new Chl-a algorithms can provide more accurate assessments in biological and biogeochemical processes such as primary productivity, phytoplankton phenology, phytoplankton dynamics, and carbon cycles.

Multiscale relationships between humpback whales and forage species hotspots within a large marine ecosystem.

Fluctuations in prey abundance, composition, and distribution can impact predators, and when predators and fisheries target the same species, predators become essential to ecosystem-based management. Because of the difficulty in collecting concomitant predator-prey data at appropriate scales in patchy environments, few studies have identified strong linkages between cetaceans and prey, especially across large geographic areas. During summer 2018, a line-transect survey for cetaceans and coastal pelagic species was conducted over the continental shelf and slope of British Columbia, Canada, and the US West Coast, allowing for a large-scale investigation of predator-prey spatial relationships. We report on a case study of humpback whales (Megaptera novaeangliae) and their primary prey-Pacific herring (Clupea pallasii), northern anchovy (Engraulis mordax), and krill-using generalized additive models to explore the relationships between whale abundance on 10-km transect segments and prey metrics. Prey metrics included direct measures of biomass densities on segments and an original hotspot metric. For each prey species, segments in the upper fifth percentile for biomass density (across all segments) were designated hotspots, and whale counts on a segment were evaluated for their relationship to number of hotspot segments (species-specific and multispecies) within 25, 50, or 100 km. Whale abundance was not strongly related to direct measures of biomass densities, whereas models using hotspot metrics were more effective at describing variation in whale abundance, underscoring that evaluating prey at relevant and measurable scales is critical in patchy, dynamic marine environments. Our analysis highlighted differences in the distribution and prey availability for three humpback whale distinct population segments (DPSs) as defined under the US Endangered Species Act, including threatened and endangered DPSs that forage within the California Current Large Marine Ecosystem. These linkages provide insights into which prey species whales may be targeting in different regions and across multiple scales and, consequently, how climatic variability and anthropogenic risks may differentially impact these distinct predator-prey assemblages. By identifying scale-appropriate prey hotspots that co-occur with humpback whale aggregations, and with targeted, consistent prey sampling and estimations of potential consumption rates by whales, these findings can help inform the conservation and management of humpback whales within an ecosystem-based management framework.

Discrimination of Oceanic Whitecaps Derived by Sea Surface Wind Using Sentinel‐2 MSI Images

AbstractOceanic whitecaps, as a manifestation of the wind‐wave breaking process and the medium of air‐sea exchange, play an important role in sea surface research. The observational data of oceanic whitecap coverage are generally in situ photographs or videos. The use of spaceborne optical sensors to capture oceanic whitecaps could provide more spatial distribution and dynamic marine environment information. In this study, more than 100 Sentinel‐2 Multi‐Spectral Instrument (MSI) images and corresponding synchronous buoy wind speed data were collected to investigate oceanic whitecaps. A curvature image processing method was employed to successfully identify oceanic whitecaps in MSI images and the whitecap coverage was estimated based on the whitecap recognition results. We established a power‐law model of MSI‐estimated whitecap coverage (W) and whitecap number density with buoy‐measured wind speed at a height of 10 m above the sea surface (U10). In addition, the differences between the MSI‐derived model and in situ measurement‐derived models which are mainly caused by the scale effect of optical remote sensing, are discussed. The whitecap‐wind model demonstrates that MSI images can improve the quantification of sea surface wind speed with high spatial resolution. MSI images were used to estimate regional high‐spatial resolution (4 km) wind speeds, and the merged results (0.25°) were verified with the hourly wind products of the fifth generation European ECMWF (Centre for Medium‐Range Weather Forecasts) atmospheric reanalysis (ERA5). Moreover, the influence of sunglint on whitecap recognition and whitecap coverage was also analyzed. The reflectance of whitecap‐affected pixels in MSI images increased obviously with wind speed compared with background seawater in weak sunglint reflection area. These results indicated that the parameters of oceanic whitecaps derived from high‐spatial resolution optical images can be used to estimate wind speed and provide a new approach for monitoring marine dynamic environments in the future.

A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans

The smart ocean has been regarded as an integrated sensing, communication, and computing ecosystem developed for connecting marine objects in surface and underwater environments. The development of the smart ocean is expected to support a variety of marine applications and services such as resource exploration, marine disaster rescuing, and environment monitoring. However, the complex and dynamic marine environments and the limited network resources raise new challenges in marine communication and computing, especially for these computing-intensive and delay-sensitive tasks. Recently, the space–air–ground–sea integrated networks have been envisioned as a promising network framework to enhance the communication and computing performance. In this paper, we conduct a comprehensive survey on the integrated sensing, communication, and computing networks (ISCCNs) for smart oceans based on the collaboration of space–air–ground–sea networks from four domains (i.e., space layer, aerial layer, sea surface layer, and underwater layer), and five aspects (i.e., sensing-related, communication-related, computation-related, security-related, and application-related). Specifically, we provide the key technologies for the ISCCNs in smart oceans, and introduce the state-of-the-art marine sensing, communication, and computing paradigms. The emerging challenges with the potential solutions of the ISCCNs for smart oceans are illustrated to enable the intelligent services. Moreover, the new applications for the ISCCNs in smart oceans are discussed, and potential research directions in smart oceans are provided for future works.

Seasonality of water column methane oxidation and deoxygenation in a dynamic marine environment

Most of the methane input to the world’s oceans is intercepted by microorganisms in sediment and the overlying water column and oxidized before it has an opportunity to reach the atmosphere, where it acts as a greenhouse gas. The factors controlling methane consumption in the ocean are not well established and its biogeochemistry in dynamic marine environments is understudied in-part because of challenges in capturing spatial and temporal variability. Our study focused on the factors that structure methane’s biogeochemistry in a dynamic marine environment, the Santa Barbara Basin. The deep-water column of the Santa Barbara Basin experiences seasonal oxygen loss and episodic replenishment which we found to be major factors in structuring the accumulation of methane and the rate at which microorganisms consumed that methane. We found the gradual decline in oxygen that commonly occurs through the summer culminated with a pronounced accumulation of methane in the water column during the fall. Rates of methane oxidation remained low in summer, increased with the buildup of methane in fall, and remained elevated into spring, even after methane concentration had declined. However, results from methane oxidation kinetics experiments revealed a zero-order kinetic dependence on oxygen concentration, indicating that oxygen’s effect on methanotrophy at the ecosystem scale is likely indirect. We also captured an apparent mixing event during fall that drove spatial and temporal variability in oxygen, nitrate and methane concentrations in the Santa Barbara Basin, with stark variations at the investigated timescale of 8 days and along isobaths at a spatial scale of 7 km. Collectively, these results indicate the seasonal development and attenuation of a methanotrophic community associated with restricted circulation, but also of a spatiotemporal variability not previously appreciated for this environment.

Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data

Understanding the spatiotemporal distributions of migratory marine species at marine renewable energy sites is a crucial step towards assessing the potential impacts of tidal stream turbines and related infrastructure upon these species. However, the dynamic marine conditions that make tidal channels attractive for marine renewable power development also make it difficult to identify and follow species of marine fishes with existing technologies such as hydroacoustics and optical cameras. Acoustic telemetry can resolve some of these problems. Acoustic tags provide unique individual ID codes at an ultrasonic frequency, which are then detected and recorded by acoustic receivers deployed in the area of interest. By matching detection locations of fish species with environmental conditions at proposed sites for tidal energy infrastructure, species distribution models can be developed to predict the probability of species occurrence at sites of current and planned tidal power development. This information can be used to develop statistically robust encounter rate models to aid in quantifying the risk of tidal power development to migratory fish species. We used this approach to develop a predictive model of striped bass (Morone saxatilis) distribution within Minas Passage in the upper Bay of Fundy, Nova Scotia. Model results suggested increased probability of striped bass presence in Minas Passage during late ebb tide conditions and at relatively high water temperatures. We demonstrate the potential utility of species distribution modeling of acoustic tag detections in predicting interactions with renewable energy infrastructure, and show the importance of physical oceanographic variables influencing species distributions in a highly dynamic marine environment.