Oceanographic Research Articles

Large-Eddy Simulation of Low-Frequency Flow Oscillations for NACA0012 and Dynarig Sail at Large Attack Angles

Unmanned sailboats, harnessing wind for propulsion, offer great potential for extended marine research due to their virtually unlimited endurance. The sails typically operate at high attack angles, which contrasts with aircraft that maintain small angles to prevent stalling. Despite the reduction in lift during stalling, the resultant increase in drag contributes significantly to the sail’s thrust. However, the sail often experiences vortex shedding due to high attack angles, leading to low-frequency oscillations and erratic navigation. This study employs large-eddy simulations (LESs) on a 3D NACA0012 sail at a Reynolds number of 3.6 × 105, which is validated by experimental data. It observes the lift and drag coefficients across attack angles from 5 to 90 degrees and compares these with a Dynarig sail. The findings reveal that higher attack angles amplify fluctuations in lift and drag coefficients. Vortex shedding, resulting from flow separation, creates pressure changes and oscillations in aerodynamic forces. Fast Fourier transformation (FFT) analysis identifies dominant frequencies between 0.5 and 10 Hz, indicating low-frequency oscillations. The study’s insights into the impact of attack angle and sail type on the oscillation frequency are favorable for the design of unmanned sailboats, aiding in the prediction of wind-induced frequencies and optimal attack angle determination.

A Review of Path Planning Methods for Marine Autonomous Surface Vehicles

A marine autonomous surface vehicle (ASV) is a kind of autonomous marine robot with intelligent and flexible use advantages. They are mainly divided into two categories: unmanned vessels and unmanned sailboats. Marine ASVs are essential in marine science, industry, environmental protection, and national defense. One of the primary challenges faced by marine ASVs is autonomously planning paths in an intricate marine environment. Numerous research findings have surfaced in recent years, including the combination with popular machine learning. However, a systematic literature review is still lacking, primarily a comprehensive comparison of two types of ASV path planning methods. This review first introduces the problem and evaluation indicators of path planning for ASVs. Then, aiming at unmanned vessels and sailboats, respectively, it sorts out various path planning algorithms proposed in the existing literature, including the advantages and limitations of both kinds of ASVs, and discusses them in combination with evaluation indicators. Also, this paper explores how marine environmental factors affect path planning and its corresponding treatment methods. Finally, this review summarizes the challenges of unmanned ship path planning, proposes potential technical solutions and future development directions, and aims to provide references for further development in this field.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Effect of Different Feeds on Growth Rate and Survival of Common Carp (Cyprinus carpio L.) Larvae

The present study was conducted to evaluate the growth and survival rates of common carp (Cyprinus carpio) larvae fed different diets levels in laboratory Two-day-old larvae were transferred from the University of Basrah Marine Science Center hatchery to the Abi Al-Khaseeb district's experiment laboratory. The larvae were distributed into four treatments 450 larvae each treatment (150 larvae per replicate and three replicates per treatment) at a stock density of 3.33 larvae /L. The treatments were as follows: T1 was given boiling egg yolks; T2 was given a processed diet with 45% protein; T3 was given Redi-Lac milk powder; and T4 was given ground Artemia eggs. The experiment ran for 20 days, beginning on April 17, 2022.The results showed that larvae fed on diet containing ground Artemia eggs (T4) produced the highest final weight (39.667mg), followed by a diet containing 45% protein processed diet (T2), , Redi-Lac milk powder food (T3), and boiled egg yolks food (T1) produced the lowest final weight ( 27.333 and 24.667 mg, respectively) Statistical analysis revealed a significant difference (P≤0.05) in the final weight, weight gain, daily growth rate, relative growth rate and specific growth rate between T4 and T1, but not between T4 and T2 and between T4 and T3. The survival rate showed a significant difference (P≤0.05) between T4 (0.71%) and other treatments. Our results suggest that ground Artemia eggs is the most suitable and optimal meal for common carp fish larvae.

The COVID‐19 Pandemic and Its Impact on National Science Foundation's Ocean Science Division Research Experiences for Undergraduates

The COVID‐19 Pandemic and Its Impact on National Science Foundation's Ocean Science Division Research Experiences for Undergraduates

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

Transdisciplinary Research in Marine Science: What’s the added value of involving stakeholders?

The holistic solution of complex, global problems in marine topics requires innovative research formats - which can be served by transdisciplinarity. To assess the success of the transdisciplinary approach for marine research, several case studies from marine research at Kiel University (Germany) were analysed for this study. Interviews with both scientists and stakeholders were carried out to 1) clarify whether stakeholder involvement in scientific projects provides additional knowledge and to 2) identify the underlying success factors. Both groups see added value in transdisciplinary projects and name positive aspects of cooperation, mutual interaction and information exchange. Essential are the applicability of research results, and the formation and maintenance of networks enabling further joint activities. Stakeholders add practical contexts to scientific knowledge so that the results of this transdisciplinary research can be translated into practical actions. Resource availability sets clear limits within the project framework. Successful transdisciplinary approaches require standardized definitions of terms, the selection of suitable participation formats and continuous, clear communication. Transdisciplinary projects proved suitable as a tool for sensitizing the general public and raising awareness of complex marine challenges.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

A Comparison of new and age-old approaches treating microplastics in the marine environment

In recent years, microplastic pollution in the ocean has become a new environmental threat, posing a serious potential threat to marine ecology. Due to its high degradation resistance and bioaccumulation, it is an urgent and critical issue for the environment. Unfortunately, current technologies for removing, recycling, or degrading microplastics are not sufficient to eliminate them. The main sources of microplastics (MPs) in the ocean are ocean drift inputs, river inputs, fishing and shipping activities, microplastic emissions from human settlements, and atmospheric migration. Microplastics may pose a threat to marine life and affect the stability of marine ecosystems. Therefore, it is urgent to further study governance methods and analysis processes, and strengthen the source control of marine microplastic pollution. This study aims to analyze and discuss different methods for treating marine microplastics, analyze their respective advantages and limitations, and evaluate their feasibility and effectiveness in practical applications. The study ultimately emphasized the importance of effective management of marine microplastics, which is crucial for protecting marine ecosystems and human health. Through a comprehensive analysis of physical, chemical, and biological methods, this study provides valuable insights for marine environmental management and research institutions, thereby promoting the development and application of effective management technologies to achieve sustainable environmental outcomes.

A Bioinspired Control Strategy Ensures Maneuverability and Adaptability for Dynamic Environments in an Underactuated Robotic Fish

Bioinspired underwater robots can move efficiently, with agility, even in complex aquatic areas, reducing marine ecosystem disturbance during exploration and inspection. These robots can improve animal farming conditions and preserve wildlife. This study proposes a muscle-like control for an underactuated robot in carangiform swimming mode. The artifact exploits a single DC motor with a non-blocking transmission system to convert the motor’s oscillatory motion into the fishtail’s oscillation. The transmission system combines a magnetic coupling and a wire-driven mechanism. The control strategy was inspired by central pattern generators (CPGs) to control the torque exerted on the fishtail. It integrates proprioceptive sensory feedback to investigate the adaptability to different contexts. A parametrized control law relates the reference target to the fishtail’s angular position. Several tests were carried out to validate the control strategy. The proprioceptive feedback revealed that the controller can adapt to different environments and tail structure changes. The control law parameters variation accesses the robotic fish’s multi-modal swimming. Our solution can vary the swimming speed of 0.08 body lengths per second (BL/s), and change the steering direction and performance by an angular speed and turning curvature radius of 0.08 rad/s and 0.25 m, respectively. Performance can be improved with design changes, while still maintaining the developed control strategy. This approach ensures the robot’s maneuverability despite its underactuated structure. Energy consumption was evaluated under the robotic platform’s control and design. Our bioinspired control system offers an effective, reliable, and sustainable solution for exploring and monitoring aquatic environments, while minimizing human risks and preserving the ecosystem. Additionally, it creates new and innovative opportunities for interacting with marine species. Our findings demonstrate the potential of bioinspired technologies to advance the field of marine science and conservation.

Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003–2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 94.4 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

Editorial: Deep learning for marine science

Editorial: Deep learning for marine science

Reconstruction of Wide Swath Significant Wave Height From Quasi‐Synchronous Observations of Multisource Satellite Sensors

AbstractGlobal sea wave monitoring is of utmost importance for tasks such as analysis of ocean climate change, offshore fisheries, and early warning of marine disasters. Significant wave height (SWH) is one of the most vital and widely used metrics for measuring sea waves in marine research. Hence, obtaining high precision and extensive coverage measurements of SWH is of great significance for comprehensive sea wave studies. A data set is constructed by combining wave spectrometer and scatterometer data from China‐French Ocean Satellite, synthetic aperture radar (SAR) wave mode data from Sentinel‐1, and altimeter data from Jason‐3 and HY‐2B through space‐time matching method. The multi‐sources integrated data set is used to complete the reconstruction of the wide swath SWH. A model based on stacked autoencoder and deep neural network (SAE‐DNN) is developed. The SWH reconstructed by the model is evaluated with the training‐independent test set. The results demonstrate that the accuracy of the SAE‐DNN model is significantly improved by incorporating SAR joint quasi‐synchronous observations and the root mean square error can reach 0.217 m, which is comparable to the SWH measured by altimeter and highlights the effectiveness and reliability of the model in accurately reconstructing SWH. We further examine and analysis the distance variations between SWH reconstruction sites and Surface Wave Investigation and Monitoring observations, the influence of different SAR features, and the influence of sea state, highlighting the benefits of incorporating SAR data into SAE‐DNN model.

From citizen science to AI models: Advancing cetacean vocalization automatic detection through multi-annotator campaigns

Continuous underwater Passive Acoustic Monitoring (PAM) has emerged as a strong tool for cetacean research. To handle the vast volume of collected data, it is essential to employ automated detection and classification methods. The recent advancement of deep learning, involving model training and testing, requires a large amount of labeled data. These labels are derived through the manual annotation of audio files often reliant on human experts. Based on an annotation campaign focusing on blue whale calls in the Indian Ocean involving 19 novice annotators and an expert in bioacoustics, this study explores the integration of novice annotators in marine bioacoustics research, through citizen science programs, which could drastically increase the size of labeled datasets and enhance the performance of detection and classification models. The analysis reveals distinctive annotation profiles influenced by the complexity of vocalizations and the annotators' strategies, ranging from conservative to permissive. To address the challenges of annotation discrepancies, Convolutional Neural Networks (CNNs) are trained on annotations from both novices and the expert. The results show variations in model performance. Our work highlights the importance of annotation guidelines encouraging a more conservative approach to improve overall annotation quality. In an effort to optimize the potential of multi-annotation and mitigate the presence of noisy labels, two annotation aggregation methods (majority voting and soft labeling) are proposed and tested. The results demonstrate that both methods, particularly when a sufficient number of annotators are involved, significantly improve model performance and reduce variability: the standard deviation of the area under PR and ROC curves fall under 0.02 for both vocalizations with 13 aggregated annotators, while it was at 0.17 and 0.21 for the Blue Whale Dcalls and 0.05 and 0.04 for the SEIO PBW vocalizations with all annotator separately. Moreover, these aggregation methods enable the training of models using non-expert annotations that achieve performances of models trained with expert annotations. These findings suggest that crowdsourced annotations from novice annotators can be a viable alternative to expert annotations.

Water quality monitoring with an early warning system for enhancing the shrimp aquaculture production

Aquaculture provided 43% of the aquatic animal food consumed by humans in 2007, and it is anticipated to expand even more to meet the increasing future demand. Marine Science Techno Park (MSTP) is one of the Techno Parks in Indonesia and is located in Teluk Awur, Jepara. MSTP has an intensive system of Vannamei shrimp farming activities. The challenge that has been faced annually is the organic matter of the remaining shrimp feed that accumulates in the waters can cause a decrease in pond water quality. Ammonia-N anions derived from the decomposition of feed residues can cause toxins in shrimp culture ponds which can further interfere with shrimp survival. The objective of this study is to design a self-controlled water quality monitoring system that is equipped with an early warning system based on internet of things (IoT) and to implement the design for analyzing the water quality including dissolved oxygen (DO), pH, and temperature in the Vannamei farming pond of MSTP UNDIP through integrating IoT which is equipped with an early warning system. If the water quality reaches a threshold value, the monitoring system will send a sound signal to the buzzer followed by sending an alert notification in real-time conditions automatically to a smartphone.

Preface

The purpose of the three-day 2023 2nd International Conference on Acoustics, Fluid Mechanics and Engineering (AFME 2023), held during November 17th to 19th, 2023 in Nanjing, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on acoustics, fluid mechanics and engineering.Highly eventful program of the Conference, the list of well-established organizations-participants and devoted engagement of many colleagues throughout all the stages of the Conference preparation instill confidence in practical importance of mutual initiative. As usual, the program of the meeting mainly consisted of plenary speeches, invited speeches, oral presentations and poster presentations, discussing significant problems facing technologies related to areas of acoustics, fluid mechanics and engineering, proposing innovative ideas and approaches to solution of the problems, and considering new possibilities of application and development of cutting-edge technology.In the framework of experimental and theoretical approaches, the Conference gathered 150 delegates from all over the world and addressed a number of highly relevant aspects of acoustics, fluid mechanics and engineering. Covering topics on Acoustic Materials, Hydrodynamic Acoustics, Ultrasonics, Physical Chemical Hydrodynamics, Hydromechanical Hydrodynamics, Engineering Fluid Mechanics, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of acoustics, fluid mechanics and engineering should be considered.We received various manuscripts of invited papers and other contributions presented at the Conference. All of these papers have gone through a rigorous peer review process and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by relevant communities.We would like to express our thanks to all the authors for their time and genuine contributions, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Guangdong University of Technology, Dalian Maritime University, School of Marine Science and Technology, Northwestern Polytechnical University, and University of Toronto.List of Committee Member is available in this pdf.

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMG), two piezoelectric nanogenerators (PENGs), and two multilayer‐structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm-3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

A Tool to Characterize Learning Objectives in Marine Science Education: Marine Science Learning Objectives Tool (MS-LOT)

Effective marine science teaching starts with well-written, measurable learning objectives that define outcomes for learners, inform instruction, and guide assessment. Yet, there are few resources to help marine science educators write effective learning objectives. To address this need, we created an evidence-based Marine Science Learning Objectives Tool (MS-LOT) to help marine educators evaluate their learning objectives. MS-LOT considers two critical aspects of learning objectives in marine science education: focus and assessment. As a case study, University of Georgia (UGA) Marine Extension and Georgia Sea Grant educators used MS-LOT to analyze their 207 learning objectives for 5th–12th grade educational programs. Findings revealed 1) gaps in content, science practices, and affective outcomes in instruction, 2) a focus on low-cognitive level outcomes, and 3) unmeasurable learning objectives. We respond to issues uncovered from the analysis and detail how MS-LOT will be useful for all marine educators, especially those working with K-12 learners.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

A Review of Pacific Salmon Hatcheries in British Columbia, Canada, and Interactions With Natural Populations

Pacific salmon Oncorhynchus spp. hatcheries in British Columbia (BC) have been the major activity of the Salmonid Enhancement Program since 1977. However, the present diminished abundance of Pacific salmon and loss of fisheries in BC called for a review of hatchery effectiveness. The Pacific Salmon Foundation conducted a review resulting in 15 reports publicly available on the Pacific Salmon Foundation marine science website. The review includes evaluations of effectiveness of hatchery production, evidence of interactions with natural salmon populations (including rebuilding natural populations with hatchery‐produced salmon), and future opportunities via genomics. Beyond the estimation of juvenile production and contribution to catches and spawning escapements, the ability to assess effectiveness was limited. The assessments of interactions provided a few examples of positive outcomes, but most were negative. The lack of comparative assessments in purely wild populations seriously limits interpretations of hatchery–wild interactions. Future evaluations of the hatchery effectiveness require more quantitative and integrated assessments of hatchery and wild salmon, open data sharing for public review or research, and related studies could benefit from application of new genomic technologies. This paper presents recommendations to address these needs and calls for regularized reviews of the Salmonid Enhancement Program and its net value to sustainability of BC's Pacific salmon.