Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.

Continental margins are disproportionally important for global primary production, fisheries and CO₂ uptake. However, across the Northeast Atlantic shelves, there has been an ongoing summertime decline of key biota—large diatoms, dinoflagellates and copepods—that traditionally fuel higher tropic levels such as fish, sea birds and marine mammals. Here, we combine multiple time series with in situ process studies to link these declines to summer nutrient stress and increasing proportions of picophytoplankton that can comprise up to 90% of the combined pico‐ and nanophytoplankton biomass in coastal areas. Among the pico‐fraction, it is the cyanobacterium Synechococcus that flourishes when iron and nitrogen resupply to surface waters are diminished. Our field data show how traits beyond small size give Synechococcus a competitive edge over pico‐ and nanoeukaryotes. Key is their ability to grow at low irradiances near the nutricline, which is aided by their superior light‐harvesting system and high affinity to iron. However, minute size and lack of essential biomolecules (e.g. omega‐3 polyunsaturated fatty acids and sterols) render Synechococcus poor primary producers to sustain shelf sea food webs efficiently. The combination of earlier spring blooms and lower summer food quantity and quality creates an increasing period of suboptimal feeding conditions for zooplankton at a time of year when their metabolic demand is highest. We suggest that this nutrition‐related mismatch has contributed to the widespread, ~50% decline in summer copepod abundance we observe over the last 60 years. With Synechococcus clades being prominent from the tropics to the Arctic and their abundances increasing worldwide, our study informs projections of future food web dynamics in coastal and shelf areas where droughts and stratification lead to increasing nutrient starvation of surface waters.

Here we describe a new trait‐based model for cellular resource allocation that we use to investigate the relative importance of different drivers for small cell size in phytoplankton. Using the model, we show that increased investment in nonscalable structural components with decreasing cell size leads to a trade‐off between cell size, nutrient and light affinity, and growth rate. Within the most extreme nutrient‐limited, stratified environments, resource competition theory then predicts a trend toward larger minimum cell size with increasing depth. We demonstrate that this explains observed trends using a marine ecosystem model that represents selection and adaptation of a diverse community defined by traits for cell size and subcellular resource allocation. This framework for linking cellular physiology to environmental selection can be used to investigate the adaptive response of the marine microbial community to environmental conditions and the adaptive value of variations in cellular physiology.

Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by > 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2%. This resulted in a 97.4% overall accuracy score and a 96.2% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.

Plastic pollution in the world’s oceans threatens ecosystems and biodiversity. The connected nature of the marine environment suggests that coordinated actions by countries sharing an ocean border may provide more effective pollution control than unilateral actions by any one country. However, countries often fail to cooperate, even when joint economic benefits would be higher under cooperation. Here we present a modelling framework to determine the potential economic benefits of cooperative marine plastic pollution management. The framework integrates an estimated plastic transfer matrix from a particle tracking model with game theory to derive the economic benefits of international cooperation for 16 countries bordering the North Atlantic Ocean. Subject to modelling uncertainties, a fully cooperative agreement yields aggregate annual net benefits of around $36 billion and a 64% reduction in emissions. The net benefits of cooperation persist over alternative scenarios and considering the impact of uncertainties but vary in magnitude and distribution.

Two new University of Arkansas table grape cultivars, Reliance (25% Vitis vinifera L. x 75% Vitis labrusca L.) and Saturn (78% Vitis vinifera L. x 22% Vitis labrusca L.), were stored for seven and 10 weeks at 2°C in corrugated cardboard shipping containers with five packing treatments (slow, dual, and quick-release SO₂ generators and lined and unlined controls). The SO₂ treatments consisted of placing grapes in shipping containers lined with unvented polyethylene bags, laying commercially available SO₂ generators on top of the grapes, and closing the bag. The lined control had a closed polyethylene bag surrounding the grapes but no SO₂, while the unlined control had neither SO₂ nor polyethylene bag. Reliance grapes had higher percentages of decay and shatter than Saturn grapes. Grapes stored with SO₂ generators had less decay than the controls. The unlined control had more than four times the percentage weight loss of any other treatment. Flavor was similar among all treatments. The SO₂ generators had no effect on color. Grapes that were wrapped and stored with no SO₂ had the greatest amount of berry shatter. Grapes stored with the dual-release SO₂ generators had less shatter than with the slow-release SO₂ generators. Stem appearance was maintained by the slow-release and dual-release treatments. The use of SO₂ generators had no effect on percentage soluble solids or pH.