As systems continue to grow in scale and complexity, the Systems Engineering community has turned to Model-Based Systems Engineering (MBSE) to manage complexity, maintain consistency, and assure traceability during system development. It is different from "engineering with models," which has been a common practice in the engineering profession for decades. MBSE is a holistic, systems engineering approach centered on the evolving system model, which serves as the "sole source of truth" about the system. It comprises system specification, design, validation, and configuration management. Even though MBSE is beginning to see a fair amount of use in multiple industries, specific advances are needed on multiple fronts to realize its full benefits. This paper discusses the motivation for MBSE, and its current state of maturity. It presents systems modeling methodologies and the role of ontologies and metamodels in MBSE. It presents model-based verification and validation (V&V) as an example of MBSE use. An illustrative example of the use of MBSE for design synthesis is presented to demonstrate an important MBSE capability. The paper concludes with a discussion of challenges to widescale adoption and offers promising research directions to fully realize the potential benefits of MBSE. K E Y W O R D Smodel-based systems engineering (MBSE), modeling and simulation, system integration, systemsof-systems, verification and validation

Biofouling in marine aquaculture is one of the main barriers to efficient and sustainable production. Owing to the growth of aquaculture globally, it is pertinent to update previous reviews to inform management and guide future research. Here, the authors highlight recent research and developments on the impacts, prevention and control of biofouling in shellfish, finfish and seaweed aquaculture, and the significant gaps that still exist in aquaculturalists' capacity to manage it. Antifouling methods are being explored and developed; these are centred on harnessing naturally occurring antifouling properties, culturing fouling-resistant genotypes, and improving farming strategies by adopting more sensitive and informative monitoring and modelling capabilities together with novel cleaning equipment. While no simple, quick-fix solutions to biofouling management in existing aquaculture industry situations have been developed, the expectation is that effective methods are likely to evolve as aquaculture develops into emerging culture scenarios, which will undoubtedly influence the path for future solutions. ARTICLE HISTORY

Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as action cameras and unmanned underwater devices has allowed footage to be captured efficiently and safely, without the logistical difficulties manual data collection often presents. It has, however, led to immense volumes of data being collected that require manual processing and thus significant time, labor, and money. The use of deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using an object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomized 80:20 ratios of training:validation datasets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%) and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer's performance in determining abundance was 7.1% better than human marine experts and 13.4% better than citizen scientists in single image test datasets, and 1.5 and 7.8% higher in video datasets, respectively. We show that deep learning can be a more accurate tool than humans at determining abundance and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper, and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance and have much to offer the field of aquatic ecology.

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Mangroves have among the highest carbon densities of any tropical forest. These 'blue carbon' ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land-uses can prevent future greenhouse gas emissions and increase CO 2 sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land-use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under 'business as usual' rates of mangrove loss.Emissions from mangrove loss could reach 2391 Tg CO 2 eq by the end of the century, or 3392 Tg CO 2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.