Management Of Marine Resources Research Articles

Fishing Vessel Type Recognition Based on Semantic Feature Vector

Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

Machine learning applied to species occurrence and interactions: the missing link in biodiversity assessment and modelling of Antarctic plankton distribution

BackgroundPlankton is the essential ecological category that occupies the lower levels of aquatic trophic networks, representing a good indicator of environmental change. However, most studies deal with distribution of single species or taxa and do not take into account the complex of biological interactions of the real world that rule the ecological processes.ResultsThis study focused on analyzing Antarctic marine phytoplankton, mesozooplankton, and microzooplankton, examining their biological interactions and co-existences. Field data yielded 1053 biological interaction values, 762 coexistence values, and 15 zero values. Six phytoplankton assemblages and six copepod species were selected based on their abundance and ecological roles. Using 23 environmental descriptors, we modelled the distribution of taxa to accurately represent their occurrences. Sampling was conducted during the 2016–2017 Italian National Antarctic Programme (PNRA) ‘P-ROSE’ project in the East Ross Sea. Machine learning techniques were applied to the occurrence data to generate 48 predictive species distribution maps (SDMs), producing 3D maps for the entire Ross Sea area. These models quantitatively predicted the occurrences of each copepod and phytoplankton assemblage, providing crucial insights into potential variations in biotic and trophic interactions, with significant implications for the management and conservation of Antarctic marine resources. The Receiver Operating Characteristic (ROC) results indicated the highest model efficiency, for Cyanophyta (74%) among phytoplankton assemblages and Paralabidocera antarctica (83%) among copepod communities. The SDMs revealed distinct spatial heterogeneity in the Ross Sea area, with an average Relative Index of Occurrence values of 0.28 (min: 0; max: 0.65) for phytoplankton assemblages and 0.39 (min: 0; max: 0.71) for copepods.ConclusionThe results of this study are essential for a science-based management for one of the world’s most pristine ecosystems and addressing potential climate-induced alterations in species interactions. Our study emphasizes the importance of considering biological interactions in planktonic studies, employing open access and machine learning for measurable and repeatable distribution modelling, and providing crucial ecological insights for informed conservation strategies in the face of environmental change.

Disentangling marine plastic impacts in Life Cycle Assessment: Spatially explicit Characterization Factors for ecosystem quality

Inputs of persistent plastic items to marine environments continue to pose a serious and long-term threat to marine fauna and ecosystem health, justifying further interventions on local and global scales. While Life Cycle Assessment (LCA) is frequently used for sustainability evaluations by industries and policymakers, plastic leakage to the environment and its subsequent impacts remains absent from the framework. Incorporating plastic pollution in the assessments requires development of both inventories and impact assessment methods. Here, we propose spatially explicit Characterization Factors (CF) for quantifying the impacts of plastic entanglement on marine megafauna (mammals, birds and reptiles) on a global scale. We utilize Lagrangian particle tracking and a Species Sensitivity Distribution (SSD) model along with species susceptibility records to estimate potential entanglement impacts stemming from lost plastic-based fishing gear. By simulating plastic losses from fishing hotspots within all Exclusive Economic Zones (EEZs) we provide country-specific impact estimates for use in LCA. The impacts were found to be similar across regions, although the median CF associated with Oceania was higher compared to Europe, Africa and Asia. Our findings underscore the presence of susceptible species across the world and the transboundary issue of plastic pollution. We discuss the application of the factors and identify areas of further refinement that can contribute towards a comprehensive assessment of macroplastic pollution in sustainability assessments. Degradation and beaching rates for different types of fishing gear remain a research gap, along with population-level effects on marine taxa beyond surface breathing megafauna. Increasing the coverage of impacts specific to the marine realm in LCA alongside other stressors can facilitate informed decision-making towards more sustainable marine resource management.

Profit and Sustainability Perceptions Related to the Implementation of Blue Accounting in the Fishing Industry in Palabuhanratu

Blue Accounting integrates accounting principles with marine and coastal resource management to support sustainable development goals, focusing on the sea as its ecosystem. This study aims to understand perceptions of profit and sustainability related to the implementation of Blue Accounting in the fishing industry in Palabuhanratu. Additionally, it seeks to shift the common perception that accounting is solely about numbers. The qualitative approach used in this research is the social constructivism framework, often described as interpretivism. The results indicate that fishermen and boat owners have differing views on profit and sustainability concerning Blue Accounting implementation in Palabuhanratu’s fishing industry. The study addresses the problem statement by exploring these perceptions. Profit and sustainability are inseparable points in the implementation of Blue Accounting, generating various arguments and assumptions from different stakeholders who observe the level of awareness among port users in Palabuhanratu. This research highlights the importance of integrating sustainability into profit considerations within the fishing industry.

3D meshes dataset of sagittal otoliths from red mullet in the Mediterranean Sea

This paper presents a dataset of 3D sagittal left otolith meshes from 339 individual red mullet (Mullus barbatus). These immature specimens were collected from 17 geographical areas covering the entire Mediterranean Sea. Measured biological parameters were: fish total length (TL ± 1 mm, range from 125 to 238 mm), total weight (W ± 0.1 g, range from 14.9 to 168.0 g), sex (S), sexual maturity staging (Mat). The 3D otolith dataset comprises high-resolution meshes of otoliths obtained using microtomography (29.2 μm voxel size). The data offer valuable insights into the morphological variability and population structure of red mullet populations in the Mediterranean Sea. Potential applications of the dataset include age determination, stock identification, and population connectivity analysis. These applications aim to enhance the understanding of red mullet populations and contribute to the sustainable management of marine resources in the Mediterranean Sea.

Predicted ecological consequences of wave energy extraction and climate-related changes in wave exposure on rocky shore communities

Abstract Wave energy has the potential to contribute in the transition to decarbonized electricity generation. Extracting wave energy might be expected to have ecological impacts on rocky shore intertidal communities where exposure is one of the most important factors determining species structure and composition. With global climatic change, coastal exposure is predicted to increase with greater significant wave height. The wave-exposed west coast of Orkney, Scotland, UK, is the site of pre-commercial wave device testing. Surveys of 39 rocky shore sites along this coast identified key species and abundances, and quantified exposure-modifying topographic variables. A spectral wave model was constructed to compare baseline, wave extraction, climate change, and combined scenarios. Generalized additive modelling was used to describe the relationship between species, topography, and exposure. Results show that individual species differentially respond to exposure changes with ‘winners’ and ‘losers’ at site level. Overall, community responses are expected to be far greater following predicted climatic change than to industrial-scale wave energy extraction, depending on spatial scale. In combination, energy extraction may reduce the effects of climate-change-related increases in wave exposure of rocky shores. Predicting how location-specific biotic assemblages respond to changes in wave energy as a result of long-term forcing agents provides a valuable marine resource management tool.

Bibliometric Analysis of Insularity in the European Union

The conceptualization of insularity in the context of islands has been widely studied and debated, encompassing both geographical and identity aspects. The academic discussion on the definition of insularity and isolation in the context of islands remains a current topic, along with the economic, social, legal and environmental challenges faced by insular regions. This study aims to deepen the conceptual framework of insularity and structure its issues from various scientific perspectives through quantitative bibliometric analysis. Key focus areas and research gaps have been identified, including the need for enhanced economic policies, better marine resource management, more comprehensive urban development research and improved data on social and political issues. The conclusions emphasize the importance of prioritizing intervention areas and specific policies to address the unique challenges faced by insular regions in terms of sustainable development.

Earth Observation—An Essential Tool towards Effective Aquatic Ecosystems’ Management under a Climate in Change

Numerous policies have been proposed by international and supranational institutions, such as the European Union, to surveil Earth from space and furnish indicators of environmental conditions across diverse scenarios. In tandem with these policies, different initiatives, particularly on both sides of the Atlantic, have emerged to provide valuable data for environmental management such as the concept of essential climate variables. However, a key question arises: do the available data align with the monitoring requirements outlined in these policies? In this paper, we concentrate on Earth Observation (EO) optical data applications for environmental monitoring, with a specific emphasis on ocean colour. In a rapidly changing climate, it becomes imperative to consider data requirements for upcoming space missions. We place particular significance on the application of these data when monitoring lakes and marine protected areas (MPAs). These two use cases, albeit very different in nature, underscore the necessity for higher-spatial-resolution imagery to effectively study these vital habitats. Limnological ecosystems, sensitive to ice melting and temperature fluctuations, serve as crucial indicators of a climate in change. Simultaneously, MPAs, although generally small in size, play a crucial role in safeguarding marine biodiversity and supporting sustainable marine resource management. They are increasingly acknowledged as a critical component of global efforts to conserve and manage marine ecosystems, as exemplified by Target 3 of the Kunming–Montreal Global Biodiversity Framework (GBF), which aims to effectively conserve 30% of terrestrial, inland water, coastal, and marine areas by 2030 through protected areas and other conservation measures. In this paper, we analysed different policies concerning EO data and their application to environmental-based monitoring. We also reviewed and analysed the existing relevant literature in order to find gaps that need to be bridged to effectively monitor these habitats in an ecosystem-based approach, making data more accessible, leading to the generation of water quality indicators derived from new high- and very high-resolution satellite monitoring focusing especially on Chlorophyll-a concentrations. Such data are pivotal for comprehending, at small and local scales, how these habitats are responding to climate change and various stressors.

PERAN KELEMBAGAAN LOKAL DALAM PENGELOLAAN SUMBERDAYA LAUT SEBAGAI UPAYA PENINGKATAN PEREKONOMIAN MASYARAKAT PESISIR (Studi Kasus di Kawasan Taman Nasional Wakatobi (TNW))

The rise of threats or intersections from marine resource utilization activities that are not environmentally friendly and do not pay attention to the sustainability and sustainability of marine resources results in less economic stability of coastal communities. Therefore, it is necessary to manage and supervise the utilization of potential marine resources so as to ensure the sustainability of marine resources, improve and encourage economic strengthening of coastal communities, especially fishermen. The purpose of this study is to examine the role of local institutions in marine resources management as an effort to improve the economy of coastal communities in the Wakatobi National Park area. The informants used in this study were fishermen, members or heads of fishermen groups, members or heads of customary groups, members or heads of non-governmental organizations. The types of data used include primary data and secondary data. Data collection techniques use interviews, observations, Focus Group Discussion (FGD) and literature studies. The data analysis used in this paper is qualitative descriptive analysis. The results showed that the role of local institutions in marine resources management as an effort to improve the economy of coastal communities in the Wakatobi National Park area, namely: 1) socializing the use of environmentally friendly traditional fishing gear so as to maintain minimum size and fishing season, fish populations remain sustainable, maintain fish production in the long term; 2) apply marine management and protection methods based on local wisdom (Sasi Method); 3) Advancing conservation partnership programs as an effort to encourage community welfare and conservation area sustainability; 4) encourage the involvement of fishing groups in the processing and processing of catches such as fish drying, preservation or seafood processing; 5) Support local economic empowerment, create local jobs and help coastal communities overcome poverty through cooperation with financial institutions and training institutions in coastal economic enterprises; 6) supervise and regulate the sustainable use of marine resources based on indigenous peoples as an effort to maintain the carrying capacity of the environment, livelihoods or the main economic resources of coastal communities; and 7) help promote sustainable coastal and marine tourism.

The Effect of Digitalization on Marine Ecosystem Alignment and the Role of Technology in Natural Resource Management in Sulawesi

This study aims to review the impact of digitalization on marine ecosystem management with a specific focus on Sulawesi, Indonesia. It explores how advanced technologies such as remote sensing, Geographic Information Systems (GIS), machine learning, and blockchain are revolutionizing the monitoring, predictive capabilities, and sustainability practices in marine resource management. The review highlights both the transformative potential of these technologies and the associated challenges including infrastructural limitations, skill gaps, and the lagging adaptation of policy frameworks. The integration of traditional ecological knowledge (TEK) with modern digital tools is emphasized as a critical approach for enhancing the effectiveness and acceptability of technological interventions in marine conservation efforts. The findings suggest that while digital tools offer substantial benefits for ecosystem management, their full potential can only be realized through strategic solutions to overcome existing barriers, fostering an inclusive approach that combines innovation with traditional practices. This review contributes to the understanding of digitalization's role in marine ecosystem sustainability and provides a foundation for future research directions.

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models’ performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery.

Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel Spectrograms.

Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior. The Involutional Neural Network (INN)-based deep learning model is used for classification, achieving an accuracy of up to 97% across various temporal segments. The proposed methodology is shown to be effective in accurately classifying the feeding intensities of Oplegnathus punctatus, enabling insights pertinent to aquaculture enhancement and ecosystem management. Future work may include additional feature extraction modalities and multi-modal data integration to further our understanding and contribute towards the sustainable management of marine resources.

The Co-Adaptation between Customary Community, State, and Market in Managing Marine Resources: Challenges and Opportunities

The Co-Adaptation between Customary Community, State, and Market in Managing Marine Resources: Challenges and Opportunities

Emergent constraint on oxygenation of the upper South Eastern Pacific oxygen minimum zone in the twenty-first century

As a consequence of on-going global warming, the ocean is losing oxygen, which has implications not only in terms of marine resources management and food supply but also in terms of the potentially important feedback on the global carbon cycle and climate. Of particular scrutiny are the extended zones of already low levels of oxygen called the oxygen minimum zones (OMZs) embedded in the subsurface waters of the productive Eastern Boundary Upwelling Systems (EBUS). These OMZs are currently diversely simulated by state-of-the-art Earth System Models (ESM) hampering a reliable projection of ocean deoxygenation on marine ecosystem services in these regions. Here we focus on the most emblematic EBUS OMZs of the planet, that of the South Eastern Pacific (SEP), which is under the direct influence of the El Niño Southern Oscillation (ENSO), the main climate mode on interannual timescales at global scale. We show that, despite the low consensus among ESM long-term projections of oxygen levels, the sensitivity of the depth of the upper margin (oxycline) of the SEP OMZ to El Niño events in an ensemble of ESMs can be used as a predictor of its long-term trend, which establishes an emergent constraint for the SEP OMZ. Because the oxycline along the coast of Peru and Chile deepens during El Niño events, the upper bound of the SEP OMZ is thus likely to deepen in the future climate, therefore oxygenating the SEP OMZ. This has implications not only for understanding the nitrogen and carbon cycles at global scale but also for designing adaptation strategies for regional upper-ocean ecosystem services.

Policy and governance of maritime resources in the Riau Islands Province

ABSTRACT This research refers to the policies carried out and the existing management in Riau Islands Province. The implementation process in managing the maritime sector must refer to Law Number 23 of 2014 which regulates Regional Government. The purpose of this research also explains what steps must be taken in resolving problems in managing natural resources that occur. This research method uses qualitative methods with qualitative analysis, the data analysis process is used purposively, namely by consideration. The results showed that the policy carried out by the Riau Islands Provincial Government through the Marine and Fisheries Service, was found to have no definite authority regarding the process of managing maritime resources in Riau Islands Province, besides there was an overlap of authority, especially for its state civil servants, who had training related to capacity building and how to manage marine resources. The conclusion in this research is to respond to the applicable law, namely Number 23 of 2014, which clearly gives heavy authority to the water area. Thus, the management that occurs is less than optimal.

Developing an integrated framework for Marine Cultural Heritage (MCH) and Marine Protected Areas (MPA) across Africa and the Middle East

There is currently a significant imbalance in existing marine resource management frameworks. Across Africa and the Middle East there is a developing network of marine protected areas focused on natural heritage but with very limited engagement with area-based management for cultural heritage. Yet both natural and cultural heritage resources are subject to the same considerable threat levels from both natural and anthropogenic processes of change. The MarEA project is engaged in an extensive programme of mapping and documentation of endangered cultural heritage sites and landscapes across the MENA region. During the course of this and aligned work, its engagement with coastal communities and international practice has identified a clear need for more informed and holistic approaches to marine resource management. This study presents a model for the integrated management of natural and cultural heritage and proposes a framework for the future identification and designation of Marine Cultural Heritage in Protected Areas.

Government contribution to community empowerment based on local potential in Percut village-Deli Serdang Regency

Indonesia is a country that is very rich in marine resources. The extraordinary potential of Indonesia's natural resources can be utilized for the welfare of society. This potential helps Indonesia get out of poverty and become a more developed country. Poverty is one of the problems that until now requires action. Increased community empowerment is needed to strengthen the principle of poverty reduction based on local potential. This research aims to get an overview of how the Village government's contribution to local potential-based community empowerment in Percut Village, as well as the impact of activities carried out by the government on the community. In this research using Qualitative method through Descriptive approach. The location of this research was Percut Village, Percut Sei Tuan District, Deli Serdang Regency. This research consists of 5 resource persons. The data collection techniques used are observation, interview and documentation. The data analysis technique is carried out by data reduction, data display and conclusion drawing. Based on the results of the study, it was found that the contribution of the village government through various programs and activities, the village government provides counseling, training, empowerment and assistance in the management and sustainable use of marine resources. In addition, the impact of activities carried out by the government on the community consists of social and economic impacts. The social impact of the community's life is well maintained and the economic impact of the community has increased.

Comparative VGP models to analyse the primary productivity in the north Indian ocean and the linkages with rising sea surface temperature

The Primary Productivity of an ecosystem is an indicator of the health of the marine ecosystem and fishing yield. Primary productivity across the continental sea of the North Indian Ocean (NIO) basin was estimated using two vertically generalised production models. We examine the productivity using two non-parametric tests and present the changes using a gridded basin map. In the Behrenfeld and Falkowski (BF) approach, estimated primary productivity shows a significant decline of over 6550 km2 from 2003 to 2020, recorded spatially 100 km2 adjacent to the coastline. However, an area of 825 km2 across the exclusive economic zone of the Indian sub-continent has seen a significant rise in primary productivity. The Kameda and Ishizaka (KI) model limits productivity reduction to 2500 km2 across the NIO basin. The seasonal trends indicate reduced summer productivity across the basin. Both models point towards a significant reduction of productivity observed across the Bay of Bengal (BoB) and Arabian Sea (ARS) coastal regions owing to the damaging effects of anthropogenic-induced global warming. Secondly, simultaneous regression analysis using climate-based indicators and satellite-based NPP and Chlr-a data shows a significant reduction with high magnitude in the levels of NPP across the BoB and ARS between 1997 and 2020 due to rising sea surface temperature and a reduction of 9% and 6% in chlorophyll-a levels, respectively. These results point towards the inverse linkage between the rising SST on the primary productivity of the NIO basin and its devastating impacts on marine fisheries, especially in the BoB. These findings underscore the need for urgent action to mitigate the impacts of climate change and promote sustainable management of marine resources.

Synergizing trade and sustainability: advancing SDG 14 through international trade dynamics

This paper explores the intricate relationship between international trade and the achievement of the United Nations Sustainable Development Goal 14 (Life Below Water), focusing on the potential of trade to contribute positively to marine conservation and sustainable use of ocean resources. By examining the intersection of economic, social, and environmental dimensions, the study delves into how trade policies and practices can be aligned with environmental objectives, particularly concerning marine ecosystems. A principal challenge identified is the tension between economic growth driven by international trade and the sustainability of marine ecosystems, exacerbated by practices such as overfishing and pollution from maritime transport. Another significant challenge is the lack of robust enforcement mechanisms for existing environmental provisions in trade agreements. To address these challenges, the paper recommends the integration of stringent and enforceable environmental clauses in trade agreements, coupled with the promotion of sustainable trade practices through international cooperation and capacity building. Furthermore, the study suggests the adoption of innovative trade mechanisms, such as eco-certification and the implementation of Marine Protected Areas (MPAs), to enhance the protection of marine life, support sustainable fisheries, and reduce ocean pollution. The paper concludes with specific policy recommendations for leveraging international trade as a tool for the sustainable management of marine resources, ultimately contributing to the broader agenda of sustainable development.

MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility

The underwater environment presents unique challenges (color distortions, reduced contrast, blurriness) hindering accurate analysis. This work introduces MuLA-GAN, a novel approach leveraging Generative Adversarial Networks (GANs) and specifically adapted Multi-Level Attention for comprehensive underwater image enhancement. MuLA-GAN integrates Multi-Level Attention within the GAN architecture to prioritize learning discriminative features crucial for precise image restoration. These relevant features encompass information on local details within image regions leveraged by spatial attention and features at various scales across the entire image captured by multi-level attention. This allows MuLA-GAN to identify and enhance objects, textures, and edges obscured by underwater distortions while also reconstructing a more accurate and visually clear representation of the underwater scene by analyzing low-level information like edges and textures, as well as high-level information like object shapes and global scene information. By selectively focusing on these relevant features, MuLA-GAN excels at capturing and preserving intricate details in underwater imagery, which is essential for various marine research, exploration, and resource management applications. Extensive evaluations on diverse datasets (UIEB test, UIEB challenge, U45, UCCS) demonstrate MuLA-GAN's superior performance compared to existing methods. Additionally, a specialized bio-fouling and aquaculture dataset confirms the model's robustness in challenging environments. On the UIEB test dataset, MuLA-GAN achieves exceptional Peak Signal-to-Noise Ratio (PSNR) (25.59) and Structural Similarity Index (SSIM) (0.893) scores, surpassing Water-Net (24.36 PSNR, 0.885 SSIM). This work addresses a significant research gap in underwater image enhancement by demonstrating the effectiveness of combining GANs with specifically adapted Multi-Level Attention mechanisms. This tailored approach offers a novel and comprehensive framework for restoring underwater image quality, providing valuable insights for accurate underwater scene analysis. The source code for MuLA-GAN is publicly available on GitHub at https://github.com/AhsanBaidar/MuLA_GAN.git