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The open ocean plays a critical role in mitigating climate change by sequestering carbon dioxide (CO2) from the atmosphere for long periods of time. This carbon storage occurs over decades to millennia and relies on the physical pump that transports cold, dense, and DIC-rich waters to the deep ocean, as part of the ocean’s overturning circulation, and the biological carbon pump (BCP). The BCP encompasses a wide range of processes, from the fixation of atmospheric CO2 by phytoplankton activity to carbon sequestration in the deep ocean. Atmospheric CO2 concentrations would be about 200 ppm higher than in a world without biology, and the global climate would be much warmer by default. This study highlights the idea that BCP efficiency is enhanced by the ocean dynamics at mesoscale and submesoscale. In fact, our results suggest that frontal regions, such as those between mesoscale eddies, could lead to an important accumulation and transport of particulate organic matter (POM) from the mixed layer depth (MLD) down to depths of about 600 meters. To reach these conclusions, a multifaceted approach was applied. It included in-situ measurements and marine snow images from a BGC Argo float equipped with an Underwater Vision Profiler (UVP6), satellite altimetry data, and Lagrangian physics diagnostics. We focused our study on three intense features in marine snow distribution observed during the 17-month long float mission in the Cape Basin, southwest of Africa. These features were located in the frontal region between mesoscale eddies. Our study suggests that a particle injection pump induced by a frontogenesis-driven mechanism has the potential to enhance the effectiveness of the biological pump by increasing the depth at which carbon is injected into the water column. This work also emphasizes the importance of establishing repeated sampling campaigns targeting the interface zones between eddies. This could improve our understanding of the mechanisms involved in the deep a

Plankton size spectra are important indicators of the ecosystem state, yet such measurements are typically biased by the available sampling methods. Here, we combined individual size measurement from two zooplankton imaging approaches—in situ observation by the Underwater Vision Profiler5 and Multinet-collection supplemented by ex situ imaging via Zooscan—obtained in the global ocean, to calculate zooplankton normalized biovolume size spectra (NBSS) for all organisms larger than 1 mm. The reconstructed NBSS combining both datasets resulted in increased biomass estimates by adding organisms poorly sampled by either of the methods. The optimal values measured by both methods are used to reconstruct the zooplankton biovolume and biomass distributions. The reconstructed slopes appeared steeper and closer to those measured only by the UVP5 (+7.6%) and flatter than those obtained only from the Multinet (−20%), particularly in tropical and temperate latitudes. The main difference in tropical and temperate NBSS from the two devices is due to the fragile rhizarians that were not accounted for when using net data. When possible, we suggest using in situ and ex situ technologies together, and we provide potential indications on how to correct for missing components of the community when only one method is available.

Simultaneous measurements of marine snow (particles larger than 600 µm) morphologies, estimates of their in situ sinking speeds and midwater attenuation in export plumes were performed for the first time using a BGC-Argo float equipped with optical and imaging sensors. The float was deployed and recovered after one year drifting in the sluggish flow regime of the Angola basin. Six consecutive chlorophyll-a and particulate matter accumulation events were recorded at the surface, each followed by an export plume of sinking aggregates. Objects larger than 600 µm were classified using machine learning recognition and clustered into four morphological categories of marine aggregates. Plankton images were validated by an expert in a few broad categories. Results show that different types of aggregates were produced and exported from the different blooms. The different morphological categories of marine snow had different sinking speeds and attenuation for similar size indicating the effect of morphology on sinking speed. However, the typical size-to-sinking relationship for two of the categories and over the larger observed size range (100 µm-few mm) was also observed, indicating the importance of size for sinking. Surprisingly, calculated in situ sinking speeds were constantly in the lower range of known values usually assessed ex situ, suggesting a methodological effect which is discussed. Moving away from purely size-based velocity relationships and incorporating these additional morphological aggregates properties will help to improve mechanistic understanding of particle sinking and provide more accurate flux estimates. When used from autonomous platforms at high frequency, they will also provide increased spatio-temporal resolution for the observation of intermittent export events naturally occurring or induced by human activities associated with marine Carbon Dioxide Removal.

In this version (April, 2024): An error in the size classes defined in the ecopart_size_bins.tsv used by the size binning function (scripts/funcs_NBS.py) was corrected, the size ratio between consecutive size bins is the same across all the size ranges now.

The sinking of particulate matter from the upper ocean dominates the export and sequestration of organic carbon by the biological pump, a critical component of the Earth's carbon cycle. Controls on carbon export are thought to be driven by ecological processes that produce and repackage sinking biogenic particles. Here, we present observations during the demise of the Northeast Atlantic Ocean spring bloom illustrating the importance of storm-induced turbulence on the dynamics of sinking particles. A sequence of four large storms caused upper layer mean turbulence levels to vary by more than three orders of magnitude. Large particle (>0.1 to 10 mm) abundance and size changed accordingly: increasing via shear coagulation when turbulence was moderate and decreasing rapidly when turbulence was intense due to shear disaggregation. Particle export was also tied to storm forcing as large particles were mixed to depth during mixed layer deepening. After the mixed layer shoaled, these particles, now isolated from intense surface mixing, grew larger and subsequently sank. This sequence of events matched the timing of sinking particle flux observations. Particle export was influenced by increases in aggregate abundance and porosity, which appeared to be enhanced by the repeated creation and destruction of aggregates. Last, particle transit efficiency through the mesopelagic zone was reduced by presumably biotic processes that created small particles (<0.5 mm) from larger ones. Our results demonstrate that ocean turbulence significantly impacts the nature and dynamics of sinking particles, strongly influencing particle export and the efficiency of the biological pump.

The Canary Current Large Marine Ecosystem (CCLME) is one of the most productive Large Marine Ecosystems worldwide. Assessing the abundance, biomass and distribution of zooplankton in the southern part of this system, off the coast of West Africa, remains challenging due to limited sampling efforts and data availability. However, zooplankton is of primary importance for pelagic ecosystem functioning. We applied an inversion method with combined analysis of acoustic and biological data for copepod discrimination using a bi-frequency (38 and 120 kHz) approach. Large copepods with equivalent spherical radii > 0.5 mm were identified using differences in the mean volume backscattering strength (MVBS). Regarding abundance measured by net sampling, copepods strongly dominated the zooplankton community and the large fraction account for 18%. This estimate correlated significantly with MVBS values that were obtained using an inverse algorithm. We confirmed the utility of using 38 kHz for large copepod detection. An epipelagic biomass of large copepod was estimated at 120–850 mg m -2 in March during upwelling season. It is worth noting that this estimation likely underestimates the true biomass due to inherent uncertainties associated with the measurement method. We recommend future investigations in the interest of using only nighttime data to improve the sampling pattern, particularly on the upper part of the water column (< 10 m) as well as on the shallow part of the continental shelf (< 20 m depth) not covered by fisheries vessel. Nevertheless, such high copepod biomass supports high fish production underlining the key role of copepod in the CCLME. Our results open the way to the analysis of the fluctuation and trend of copepod biomass, along with three decades of fisheries acoustics data available in the region. This helps to determine ecosystem changes, particularly under climate change, and to investigate the role of copepods in the southern CCLME carbon pump at the fin

In marine ecosystems, most physiological, ecological, or physical processes are size dependent. These include metabolic rates, the uptake of carbon and other nutrients, swimming and sinking velocities, and trophic interactions, which eventually determine the stocks of commercial species, as well as biogeochemical cycles and carbon sequestration. As such, broad-scale observations of plankton size distribution are important indicators of the general functioning and state of pelagic ecosystems under anthropogenic pressures. Here, we present the first global datasets of the Pelagic Size Structure database (PSSdb), generated from plankton imaging devices. This release includes the bulk particle normalized biovolume size spectrum (NBSS) and the bulk particle size distribution (PSD), along with their related parameters (slope, intercept, and R2) measured within the epipelagic layer (0–200 m) by three imaging sensors: the Imaging FlowCytobot (IFCB), the Underwater Vision Profiler (UVP), and benchtop scanners. Collectively, these instruments effectively image organisms and detrital material in the 7–10 000 µm size range. A total of 92 472 IFCB samples, 3068 UVP profiles, and 2411 scans passed our quality control and were standardized to produce consistent instrument-specific size spectra averaged to 1° × 1° latitude and longitude and by year and month. Our instrument-specific datasets span most major ocean basins, except for the IFCB datasets we have ingested, which were exclusively collected in northern latitudes, and cover decadal time periods (2013–2022 for IFCB, 2008–2021 for UVP, and 1996–2022 for scanners), allowing for a further assessment of the pelagic size spectrum in space and time. The datasets that constitute PSSdb's first release are available at https://doi.org/10.5281/zenodo.11050013 (Dugenne et al., 2024b). In addition, future updates to these data products can be accessed at https://doi.org/10.5281/zenodo.7998799.

Dimensions of particulate matter found in the water column of marine and freshwater environments (the pelagic realm) range from nanometers to tens of meters. Included in this enormous size range are miniature bacteria, phytoplankton (photosynthetic microalgae), mixoplankton (mixotrophic microorganisms), micro- to meter sized drifting animals (zooplankton), plastic particles, detrital aggregates and fecal pellets, fish, whales and many others. These particles and organisms are involved in many different processes and perform a multitude of services, such as in oceanic biogeochemistry (carbon fixation, oxygen production, carbon export and others) or human nourishment (fisheries). Digital optical tools used in pelagic imaging approaches now allow to bridge this enormous size span and to image micro- to meter-sized objects in situ or on discrete samples. Monitoring plankton, nekton, and particle dynamics at spatial and temporal scales that enable effective management of marine and freshwater environments poses a collective challenge for society. We here argue that a global, distributed and operational network for pelagic imaging is needed and within reach, and we provide recommendations how it can be attained via the voluntary activities of the pelagic imaging community and strategic support from funding agencies and other stakeholders.

Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods. The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton functional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POCS and by 22 % for POCL. Results were less robust over the equatorial Pacific and some parts of the high latitudes. For POCS reconstruction, the most important drivers were the depth level, temperature, microzooplankton and PO4, while for POCL it was the depth level, temperature, mixed-layer depth, microzooplankton, phaeocystis, PO4 and chlorophyll a averaged over the mixed-layer depth. These results suggest that it will be possible to

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

Plastic is a widespread marine pollutant, with most studies focusing on the distribution of floating plastic debris at the sea surface. Recent evidence, however, indicates a significant presence of such low density plastic in the water column and at the seafloor, but information on its origin and dispersion is lacking. Here, we studied the pathways and fate of sinking plastic debris in the Mediterranean Sea, one of the most polluted world seas. We used a recent Lagrangian plastic-tracking model, forced with realistic parameters, including a maximum estimated sinking speed of 7.8 m/d. Our simulations showed that the locations where particles left the surface differed significantly from those where they reached the seafloor, with lateral transport distances between 119 and 282 km. Furthermore, 60% of particles deposited on the bottom coastal strip (20 km wide) were released from vessels, 20% from the facing country, and 20% from other countries. Theoretical considerations furthermore suggested that biological activities potentially responsible for the sinking of low density plastic occur throughout the water column. Our findings indicate that the responsibility for seafloor plastic pollution is shared among Mediterranean countries, with potential impact on pelagic and benthic biota.

The total mass flux (TMF) of particulate organic matter (POM) is key for understanding the energetic transfer within the “biological pump” (i.e., involving the carbon cycle), reflecting a critical connection between the surface and the bottom. A fixed multi-sediment trap was installed at 30 m depth in Callao Bay, central Peru from March to December 2020. After recovery, samples were dried and weighed to calculate the TMF and pellet flux. The average TMF was 601.9 mg·m−2·day−1, with 70.2 and 860 mg·m−2·day−1 as the lowest and highest values during “normal conditions”. Zooplankton fecal pellets (ZFP) were found in ovoid (e.g., larvae) and cylindrical (e.g., adult copepods) shapes and their flux contribution to TMF was low, ranging from 0.17 to 85.59 mg·m−2·day−1. In contrast with ZFP, fish fecal pellets (FFP) were found in fragments with a cylindrical shape, and their contribution to the TMF was higher than ZFP, ranging from 1 to 92.56 mg·m−2·day−1. Mean sinking velocities were 4.63 ± 3.47 m·day−1 (ZFP) and 432.27 ± 294.26 m·day−1 (FFP). There is a considerable difference between the ZFP and FFP contributions to TMFs. We discuss the implications of these results regarding a still poorly understood process controlling the POM flux off the Peruvian coast.

Zooplankton and nekton organisms play multifaceted roles in marine ecosystems and are integral components of the ocean’s food web. By consuming a wide range of planktonic organisms and detrital matter, they directly impact the size-distribution of particles in the ocean by breaking large aggregates down to smaller fragments and by repackaging single phytoplankton cells into dense fecal pellets. Many zooplankton and nekton organisms also conduct diel vertical migrations (DVM). They ascend to the surface layer of the ocean at dusk to feed during the dark hours and return to midwater depths at dawn to hide from visual predation. As they metabolize and excrete organic material in deeper waters, they contribute to an “active flux” of carbon and nutrients. This active flux can have a substantial impact on the functioning of the biological pump — the process responsible for the downward export of carbon and nutrients into the ocean’s interior. In essence, zooplankton and nekton are gatekeepers of the biological pump via their diverse roles in particle dynamics, from consumption and fragmentation to the active transport of organic matter. Understanding these roles is critical for unraveling the complex mechanisms that govern the health and functioning of marine ecosystems, as well as for improving our models of global biogeochemical processes in the world’s oceans.