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Prioritizing groupings of organisms or 'units' below the species level is a critical issue for conservation purposes. Several techniques encompassing different time-frames, from genetics to ecological markers, have been considered to evaluate existing biological diversity at a sufficient temporal resolution to define conservation units. Given that acoustic signals are expressions of phenotypic diversity, their analysis may provide crucial information on current differentiation patterns within species. Here, we tested whether differences previously delineated within dolphin species based on i) geographic isolation, ii) genetics regardless isolation, and iii) habitat, regardless isolation and genetics, can be detected through acoustic monitoring. Recordings collected from 104 acoustic encounters of Stenella coeruleoalba, Delphinus delphis and Tursiops truncatus in the Azores, Canary Islands, the Alboran Sea and the Western Mediterranean basin between 1996 and 2012 were analyzed. The acoustic structure of communication signals was evaluated by analyzing parameters of whistles in relation to the known genetic and habitat-driven population structure.

Visual detection technology is essential for an unmanned surface vehicle (USV) to perceive the surrounding environment; it can determine the spatial position and category of the object, which provides important environmental information for path planning and collision prevention of the USV. During a close-in reconnaissance mission, it is necessary for a USV to swiftly navigate in a complex maritime environment. Therefore, an object detection algorithm used in USVs should have high detection s peed and accuracy. In this paper, a YOLOv5 lightweight object detection algorithm using a Ghost module and Transformer is proposed for USVs. Firstly, in the backbone network, the original convolution operation in YOLOv5 is upgraded by convolution stacking with depth-wise convolution in the Ghost module. Secondly, to exalt feature extraction without deepening the network depth, we propose integrating the Transformer at the end of the backbone network and Feature Pyramid Network structure in the YOLOv5, which can improve the ability of feature expression. Lastly, the proposed algorithm and six other deep learning algorithms were tested on ship datasets. The results show that the average accuracy of the proposed algorithm is higher than that of the other six algorithms. In particular, in comparison with the original YOLOv5 model, the model size of the proposed algorithm is reduced to 12.24 M, the frames per second reached 138, the detection accuracy was improved by 1.3%, and the mean of average precision (0.5) reached 96.6% (from 95.3%). In the verification experiment, the proposed algorithm was tested on the ship video collected by the "JiuHang 750" USV under different marine environments. The test results show that the proposed algorithm has a significantly improved detection accuracy compared with other lightweight detection algorithms.

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

With the development of the marine economy, video surveillance has become an important technical guarantee in the fields of marine engineering, marine public safety, marine supervision, and maritime traffic safety. In video surveillance, maritime object detection (MOD) is one of the most important core technologies. Affected by the size of maritime objects, distance, day and night weather, and changes in sea conditions, MOD faces challenges such as false detection, missed detection, slow detection speed, and low accuracy. However, the existing object detection algorithms usually adopt predefined anchor boxes to search and locate for objects of interest, making it difficult to adapt to maritime objects' complex features, including the varying scale and large aspect ratio difference. Therefore, this paper proposes a maritime object detection algorithm based on the improved convolutional neural network (CNN). Firstly, a differential-evolutionary-based K-means (DK-means) anchor box clustering algorithm is proposed to obtain adaptive anchor boxes to satisfy the maritime object characteristics. Secondly, an adaptive spatial feature fusion (ASFF) module is added in the neck network to enhance multi-scale feature fusion. Finally, focal loss and efficient intersection over union (IoU) loss are adopted to replace the original loss function to improve the network convergence speed. The experimental results on the Singapore maritime dataset show that our proposed algorithm improves the average precision by 7.1%, achieving 72.7%, with a detection speed of 113 frames per second, compared with You Only Look Once v5 small (YOLOv5s). Moreover, compared to other counterparts, it can achieve a better speed-accuracy balance, which is superior and feasible for the complex maritime environment.

We analysed Distributed Acoustic Sensing (DAS) data from a fibre optic sensing system deployed on an existing submarine cable located offshore Oregon to characterize fin whale calls. A sequence of over 300 calls in a 2-hour period was identified using the conventional earthquake detection technique of template matching. With these initial detections we then used a robust correlation, and stacking process to estimate the call signatures and timings. Calls were found to be of two distinct types that are typical for fin whales and referred to as doublets. The calls typically alternate between the two types with an inter-call interval of approximately 15 seconds. These sequences pause approximately every 12 minutes for a couple of minutes before recommencing. These breaks are interpreted to be the whale resurfacing to breath. We track the whale's location over two hours using conventional location methods from time picks derived from a correlation process. This shows that the whale moved eastwards, towards the Pacific coastline, before turning to the south. Coincidentally, during this time frame a large container vessel also traverses the submarine fibre optic cable. The distance between the vessel and the whale ranges between 16 km and 2km at the closest point of approach. The whale initially appears to turn north as the vessel approaches to within 10km of the vessel and then follows an erratic localized track before proceeding in a southward direction away from the vessel. This behaviour may be indicative of an avoidance behaviour. This observation suggests fibre optic acoustic measurement systems could routinely monitor underwater radiated noise from marine traffic and marine mammals using existing seafloor cables to establish typical behavioural patterns.