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Aquatic ecologists are integrating mixotrophic plankton – here defined as microorganisms with photosynthetic and phagotrophic capacity – into their understanding of marine food webs and biogeochemical cycles. Understanding mixotroph temporal and spatial distributions, as well as the environmental conditions under which they flourish, is imperative to understanding their impact on trophic transfer and biogeochemical cycling. Mixotrophs are hypothesized to outcompete strict photoautotrophs and heterotrophs when either light or nutrients are limiting, but testing this hypothesis has been hindered by the challenge of identifying and quantifying mixotrophs in the field. Using field observations from a multi-decadal northern North Atlantic dataset, we calculated the proportion of organisms that are considered mixotrophs within individual microplankton samples. We also calculated a “trophic index” that represents the relative proportions of photoautotrophs (phytoplankton), mixotrophs, and heterotrophs (microzooplankton) in each sample. We found that the proportion of mixotrophs was positively correlated with temperature, and negatively with either light or inorganic nutrient concentration. This proportion was highest during summertime thermal stratification and nutrient limitation, and lowest during the North Atlantic spring bloom period. Between 1958 and 2015, changes in the proportion of mixotrophs coincided with changes in the Atlantic Multi-decadal Oscillation (AMO), was highest when the AMO was positive, and showed a significant uninterrupted increase in offshore regions from 1992-2015. This study provides an empirical foundation for future experimental, time series, and modeling studies of aquatic mixotrophs.

Phago-mixotrophy, the combination of photoautotrophy and phagotrophy in mixoplankton, organisms that can combine both trophic strategies, have gained increasing attention over the past decade. It is now recognized that a substantial number of protistan plankton species engage in phago-mixotrophy to obtain nutrients for growth and reproduction under a range of environmental conditions. Unfortunately, our current understanding of mixoplankton in aquatic systems significantly lags behind our understanding of zooplankton and phytoplankton, limiting our ability to fully comprehend the role of mixoplankton (and phago-mixotrophy) in the plankton food web and biogeochemical cycling. Here, we put forward five research directions that we believe will lead to major advancement in the field: (i) evolution: understanding mixotrophy in the context of the evolutionary transition from phagotrophy to photoautotrophy; (ii) traits and trade-offs: identifying the key traits and trade-offs constraining mixotrophic metabolisms; (iii) biogeography: large-scale patterns of mixoplankton distribution; (iv) biogeochemistry and trophic transfer: understanding mixoplankton as conduits of nutrients and energy; and (v) in situ methods: improving the identification of in situ mixoplankton and their phago-mixotrophic activity.

Doliolids are common gelatinous grazers in marine ecosystems around the world and likely influence carbon cycling due to their large population sizes with high growth and excretion rates. Aggregations or blooms of these organisms occur frequently, but they are difficult to measure or predict because doliolids are fragile, under sampled with conventional plankton nets, and can aggregate on fine spatial scales (1–10 m). Moreover, ecological studies typically target a single region or site that does not encompass the range of possible habitats favoring doliolid proliferation. To address these limitations, we combined in situ imaging data from six coastal ecosystems, including the Oregon shelf, northern California, southern California Bight, northern Gulf of Mexico, Straits of Florida, and Mediterranean Sea, to resolve and compare doliolid habitat associations during warm months when environmental gradients are strong and doliolid blooms are frequently documented. Higher ocean temperature was the strongest predictor of elevated doliolid abundances across ecosystems, with additional variance explained by chlorophyllafluorescence and dissolved oxygen. For marginal seas with a wide range of productivity regimes, the nurse stage tended to comprise a higher proportion of the doliolids when total abundance was low. However, this pattern did not hold in ecosystems with persistent coastal upwelling. The doliolids tended to be most aggregated in oligotrophic systems (Mediterranea and southern California), suggesting that microhabitats within the water column favor proliferation on fine spatial scales. Similar comparative approaches can resolve the realized niche of fast‐reproducing marine animals, thus improving predictions for population‐level responses to changing oceanographic conditions.

The Marine Biogeochemistry Library (MARBL) is a prognostic ocean biogeochemistry model that simulates marine ecosystem dynamics and the coupled cycles of carbon, nitrogen, phosphorus, iron, silicon, and oxygen. MARBL is a component of the Community Earth System Model (CESM); it supports flexible ecosystem configuration of multiple phytoplankton and zooplankton functional types; it is also portable, designed to interface with multiple ocean circulation models. Here, we present scientific documentation of MARBL, describe its configuration in CESM2 experiments included in the Coupled Model Intercomparison Project version 6 (CMIP6), and evaluate its performance against a number of observational data sets. The model simulates present‐day air‐sea CO₂flux and many aspects of the carbon cycle in good agreement with observations. However, the simulated integrated uptake of anthropogenic CO₂is weak, which we link to poor thermocline ventilation, a feature evident in simulated chlorofluorocarbon distributions. This also contributes to larger‐than‐observed oxygen minimum zones. Moreover, radiocarbon distributions show that the simulated circulation in the deep North Pacific is extremely sluggish, yielding extensive oxygen depletion and nutrient trapping at depth. Surface macronutrient biases are generally positive at low latitudes and negative at high latitudes. CESM2 simulates globally integrated net primary production (NPP) of 48 Pg C yr⁻¹and particulate export flux at 100 m of 7.1 Pg C yr⁻¹. The impacts of climate change include an increase in globally integrated NPP, but substantial declines in the North Atlantic. Particulate export is projected to decline globally, attributable to decreasing export efficiency associated with changes in phytoplankton community composition.

Among marine organisms, gelatinous zooplankton (GZ; cnidarians, ctenophores, and pelagic tunicates) are unique in their energetic efficiency, as the gelatinous body plan allows them to process and assimilate high proportions of oceanic carbon. Upon death, their body shape facilitates rapid sinking through the water column, resulting in carcass depositions on the seafloor (“jelly‐falls”). GZ are thought to be important components of the biological pump, but their overall contribution to global carbon fluxes remains unknown. Using a data‐driven, three‐dimensional, carbon cycle model resolved to a 1° global grid, with a Monte Carlo uncertainty analysis, we estimate that GZ consumed 7.9–13 Pg C y⁻¹in phytoplankton and zooplankton, resulting in a net production of 3.9–5.8 Pg C y⁻¹in the upper ocean (top 200 m), with the largest fluxes from pelagic tunicates. Non‐predation mortality (carcasses) comprised 25% of GZ production, and combined with the much greater fecal matter flux, total GZ particulate organic carbon (POC) export at 100 m was 1.6–5.2 Pg C y⁻¹, equivalent to 32–40% of the global POC export. The fast sinking GZ export resulted in a high transfer efficiency (T_eff) of 38–62% to 1,000 m and 25–40% to the seafloor. Finally, jelly‐falls at depths >50 m are likely unaccounted for in current POC flux estimates and could increase benthic POC flux by 8–35%. The significant magnitude of and distinct sinking properties of GZ fluxes support a critical yet underrecognized role of GZ carcasses and fecal matter to the biological pump and air‐sea carbon balance.

The rise of in situ plankton imaging systems, particularly high‐volume imagers such as the In Situ Ichthyoplankton Imaging System, has increased the need for fast processing and accurate classification tools that can identify a high diversity of organisms and nonliving particles of biological origin. Previous methods for automated classification have yielded moderate results that either can resolve few groups at high accuracy or many groups at relatively low accuracy. However, with the advent of new deep learning tools such as convolutional neural networks (CNNs), the automated identification of plankton images can be vastly improved. Here, we describe an image processing procedure that includes preprocessing, segmentation, classification, and postprocessing for the accurate identification of 108 classes of plankton using spatially sparse CNNs. Following a filtering process to remove images with low classification scores, a fully random evaluation of the classification showed that average precision was 84% and recall was 40% for all groups. Reliably classifying rare biological classes was difficult, so after excluding the 12 rarest taxa, classification accuracy for the remaining biological groups became > 90%. This method provides proof of concept for the effectiveness of an automated classification scheme using deep‐learning methods, which can be applied to a range of plankton or biological imaging systems, with the eventual application in a variety of ecological monitoring and fisheries management contexts.