Explore the vast range of titles available.

Everyone is talking about Windy Island's new surf center, even Nicky's hero, Australian surf champion Gary Moon! The artificial reef is supposed to create perfect waves. But the Thea sisters have discovered a dangerous secret: even a surf champion can't face these giant waves! It's up to them to break the story before another reef is built off the shore of their beloved Donkey Beach.

Artificial reefs are beneficial to restore fishery resources and increase fishery production. Meanwhile, they play a significant role in improving ocean ecology and accelerating the evolution of fishery industries. Since they are generally affected by currents, waves, and other hydrological factors, the flow field around artificial reefs and their stabilities have become a research hotspot in recent years. Research on artificial reefs is a systematic process consisting of four aspects: Firstly, the significance, the definition, the mechanism, and the present research progress were introduced for artificial reefs in detail. Secondly, the development trend of the sit-bottom artificial reef and that of the floating artificial reef were summarized, respectively. Thirdly, it was found that the combination of traditional artificial reefs and emerging ocean engineering has a great development potential in practical engineering. Finally, the existing problems related to the hydrodynamic characteristics of the artificial reefs in China were summarized, and the prospects of artificial reefs were proposed. The purpose of this study is to provide a scientific reference for the ecological and sustainable development of the large-scale construction of artificial reefs in the ocean.

Approaches towards habitat conservation and restoration often include supplementing or enhancing existing, degraded, or lost natural habitats. In aquatic environments, a popular approach towards habitat enhancement is the introduction of underwater human-made structures or artificial reefs. Despite the nearly global prevalence of artificial reefs deployed to enhance habitat, it remains debated whether these structures function similarly to comparable natural reefs. To help resolve this question, we conducted a literature review and accompanying meta-analysis of fish community metrics on artificial reefs within the coastal ocean and made comparisons with naturally-occurring reference reefs (rocky reefs and coral reefs). Our findings from a synthesis of 39 relevant studies revealed that, across reef ecosystems, artificial reefs support comparable levels of fish density, biomass, species richness, and diversity to natural reefs. Additional analyses demonstrated that nuances in these patterns were associated with the geographic setting (ocean basin, latitude zone) and artificial reef material. These findings suggest that, while artificial reefs can mimic natural reefs in terms of the fish assemblages they support, artificial reefs are not one-size-fits-all tools for habitat enhancement. Instead, artificial reefs should be considered strategically based on location-specific scientific assessments and resource needs to maximize benefits of habitat enhancement.

Artificial reefs serve as a crucial measure for preventing habitat degradation, enhancing primary productivity in marine areas, and restoring and increasing fishery resources, making them an essential component of marine ranching development. Accurate identification and detection of artificial reefs are vital for ecological conservation and fishery resource management. To achieve precise segmentation of artificial reefs in multibeam sonar images, this study proposes an improved YOLOv11-based model, YOLO-AR. Specifically, the DCCA (Dynamic Convolution Coordinate Attention) module is introduced into the backbone network to reduce the model’s sensitivity to complex seafloor environments. Additionally, a small-object detection layer is added to the neck network, along with the ultra-lightweight dynamic upsampling operator DySample (Dynamic Sampling), which enhances the model’s ability to segment small artificial reefs. Furthermore, some standard convolution layers in the backbone are replaced with ADown (Advanced Downsampling) to reduce the model’s complexity. Experimental results demonstrate that YOLO-AR achieves an mAP@0.5 of 0.912, an intersection-over-union (IOU) of 0.832, and an F1 score of 0.908. Meanwhile, the parameters and model size of YOLO-AR are 2.67 million and 5.58 MB. Compared to other advanced segmentation models, YOLO-AR maintains a more lightweight structure while delivering a superior segmentation performance. In real-world multibeam sonar images, YOLO-AR can accurately segment artificial reefs, making it highly effective for practical applications.

As the development of large-scale offshore wind farms (OWFs) amplifies due to technological progress and a growing demand for renewable energy, associated footprints on the seabed are becoming increasingly common within soft-bottom environments. A large part of the footprint is the scour protection, often consisting of rocks that are positioned on the seabed to prevent erosion. As such, scour protection may resemble a marine rocky reef and could have important ecosystem functions. While acknowledging that OWFs disrupt the marine environment, the aim of this systematic review was to examine the effects of scour protection on fish assemblages, relate them to the effects of designated artificial reefs (ARs) and, ultimately, reveal how future scour protection may be tailored to support abundance and diversity of marine species. The results revealed frequent increases in abundances of species associated with hard substrata after the establishment of artificial structures (i.e., both OWFs and ARs) in the marine environment. Literature indicated that scour protection meets the requirements to function as an AR, often providing shelter, nursery, reproduction, and/or feeding opportunities. Using knowledge from AR models, this review suggests methodology for ecological improvements of future scour protections, aiming towards a more successful integration into the marine environment.

Artificial reefs are now commonly used as a tool to restore degraded coral reefs and have a proven potential to enhance biodiversity. Despite this, there is currently a limited understanding of ecosystem functioning on artificial reefs, and how this compares to natural reefs. We used water sampling (bottom water sampling and pore water sampling), as well as surface sediment sampling and sediment traps, to examine the storage of total organic matter (as a measure of total organic carbon) and dynamics of dissolved inorganic nitrate, nitrite, phosphate and ammonium. These biogeochemical parameters were used as measures of ecosystem functioning, which were compared between an artificial reef and natural coral reef, as well as a degraded sand flat (as a control habitat), in Bali, Indonesia. We also linked the differences in these parameters to observable changes in the community structure of mobile, cryptobenthic and benthic organisms between habitat types. Our key findings showed: (1) there were no significant differences in inorganic nutrients between habitat types for bottom water samples, (2) pore water phosphate concentrations were significantly higher on the artificial reef than on both other habitats, (3) total organic matter content in sediments was significantly higher on the coral reef than both other habitat types, and (4) total organic matter in sediment traps in sampling periods May and September were higher on coral reefs than other habitats, but no differences were found in November. Overall, in terms of ecosystem functioning (specifically nutrient storage and dynamics), the artificial reef showed differences from the nearby degraded sand flat, and appeared to have some similarities with the coral reef. However, it was shown to not yet be fully functioning as the coral reef, which we hypothesise is due its relatively less complex benthic community and different fish community. We highlight the need for longer term studies on artificial reef functioning, to assess if these habitats can replace the ecological function of coral reefs at a local level.

Artificial reefs (AR), which are integral tools for fish management, ecological reconciliation and restoration efforts, require non-polluting materials and intricate designs that mimic natural habitats. Despite their three-dimensional complexity, current designs nowadays rely on empirical methods that lack standardised pre-immersion assessment. To improve ecosystem integration, we propose to evaluate 3-dimensional Computer-aided Design (3D CAD) models using a method inspired by functional ecology principles. Based on existing metrics, we assess geometric (C-convexity, P-packing, D-fractal dimension) and informational complexity (R-specific richness, H- diversity, J-evenness). Applying these metrics to different reefs constructed for habitat protection, biomass production and bio-mimicry purposes, we identify potential complexity target points (CTPs). This method provides a framework for improving the effectiveness of artificial reef design by allowing for the adjustment of structural properties. These CTPs represent the first step in enhancing AR designs. We can refine them by evaluating complexity metrics derived from 3D reconstructions of natural habitats to advance bio-mimicry efforts. In situ, post-immersion studies can help make the CTPs more specific for certain species of interest by exploring complexity-diversity or complexity-species distribution relationships at the artificial reef scale.

Offshore wind power, as a crucial component of clean energy, is rapidly expanding globally; however, the long-term impact mechanisms on marine benthic ecosystems remain unclear. This study focuses on four offshore wind farms (South 3, South 4, Site V, and Site U1) in the southern waters of the Shandong Peninsula. Based on benthic organism survey data and multi-source remote sensing environmental data from 2015 to 2024, a remote sensing-in-situ integrated machine learning prediction framework was constructed to systematically assess the spatiotemporal impact of wind farm construction and operation on soft-bottom benthic communities. The study employed the XGBoost model as the main model and the Generalized Additive Model (GAM) as the baseline model, using SHAP interpretability analysis to reveal key driving factors. The results show that the XGBoost model achieved an R² of 0.742 on the test set, significantly outperforming the GAM model (R²=0.625). The years in operation (YSI) was the most important factor affecting benthic community diversity; after a brief disturbance during the initial construction phase, the community showed a significant recovery trend after 2–4 years of operation. The artificial reef effect caused by the conversion to hard bottom near the pile foundations resulted in an approximately 13% increase in the Shannon diversity index and an approximately 40% increase in species richness compared to the control area. This study provides a reproducible methodological framework for the ecological impact assessment of offshore wind farms, and the findings can provide scientific basis for the environmentally friendly layout planning of offshore wind power in China.

The construction of artificial reefs (ARs) is an effective way to restore habitats and increase and breed fishery resources in marine ranches. However, studies on the impacts of ARs on the structure, function, and assembly patterns of the bacterial community (BC), which is important in biogeochemical cycles, are lacking. The compositions, diversities, assembly patterns, predicted functions, and key environmental factors of the attached and free-living microbial communities in five-year ARs (O-ARs) and one-year ARs (N-ARs) in Fangchenggang, China, were analyzed via 16S rRNA gene sequencing. Proteobacteria was the dominant taxon in all the samples, with an average relative abundance of 44.48%, followed by Bacteroidetes (17.42%) and Cyanobacteria (15.19%). The composition of bacterial phyla was similar between O-ARs and N-ARs, but the relative abundance of Cyanobacteria was greater in the water column (38.56%) than on the AR surface (mean of 7.40%). The results revealed that the Shannon‒Wiener diversity indices were 5.64 and 5.45 for O-ARs and N-ARs, respectively. Principal coordinate analysis (PCoA) revealed different distributions of O-ARs and N-ARs in the microbial community. Additionally, network analysis revealed that the bacterial community was more complex and stable in O-ARs than in N-ARs, indicating that the 5-year AR presented a more diverse and stable microbial community overall. The KEGG database was used to predict that nitrogen metabolism, carbon metabolism, and membrane transport were the dominant microbial functions, accounting for 29.93% of the total functional abundances. The results of the neutral community model revealed that stochastic processes (67.2%) dominated the assembly of BCs. Interestingly, deterministic processes may be increasingly important in community aggregation over time. Moreover, a null model revealed that dispersal limitation was the most important process among the stochastic processes, accounting for 57.14% of the total. In addition, redundancy analysis (RDA) revealed that hydrological factors obviously impacted the structure and function of the microbial community. Our results showed that the construction of ARs slightly promotes local diversities in the structure and function of the microbial community, indicating it requires a longer time to enhance the diversity of the microbial community on artificial reefs. Key points • Artificial reefs facilitate the diversity and functions of the microbial community • Stochastic processes dominate the assembly of the microbial community in artificial reefs • Nitrogen and carbon metabolism dominate microbial functions in artificial reefs

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.