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.

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 CO₂ 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 Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO_2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them.

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.

Significance: Seagrasses are important for ecosystem services, including climate regulation and fisheries production. But they are threatened by multiple pressures including poor water quality and coastal development. Seagrass extent is not monitored in many places, so areas at most risk of decline and the management actions needed in these places are largely unknown. We examine associations between change in seagrass meadow area and key pressures globally, helping predict the trajectory of meadows in unmonitored regions. We find rapidly shrinking seagrass meadows in areas where water quality is poor and destructive fishing occurs. Trajectories of change also vary with seagrass life-history strategy. Finally, we identify several unmonitored locations at risk of decline, highlighting where urgent monitoring and management are required.