A new study published on October 8 in the journal Science by researchers at the University of California San Diego’s Scripps Institution of Oceanography and the University of Lincoln offers a strikingly simple answer to part of that puzzle: the gooier the particle, the better it performs. The team found that the viscosity of marine snow—its thickness, its stickiness, its sheer sliminess—can have a decisive impact on how quickly bacteria break the particles down and how far they ultimately sink. According to the researchers’ estimates, high-viscosity marine snow can shuttle more than six times as much carbon into the deep ocean as its runnier, less viscous counterparts. In a field where global carbon budgets hinge on precisely these kinds of particle-level details, a sixfold difference tied to a single physical property is the sort of result that forces modelers and field scientists alike to rethink their assumptions about how the biological carbon pump actually works.

The breakthrough began, as many do, with a technical frustration. In 2021, Bryce Inman, then a postdoctoral scholar at Scripps Oceanography, set out to visualize what he describes as the muscoscape—the intricate, microscopic world of bacteria living within and upon mucus. The obstacle was fundamental: no one had ever directly observed individual bacteria interacting with the physical structure of mucus, whether in the ocean or anywhere else. For four years, Inman and his colleagues, including Farooq Azam, a microbiologist and professor emeritus at Scripps, attempted and failed to make such observations possible. Mucus is transparent, delicate, and notoriously difficult to image without destroying the very architecture researchers hoped to study. It was not until 2025 that the team finally cracked the problem, developing a method that allowed them to see, for the first time, the hidden topography of a particle that sinks past billions of microbes every day.

The solution combined 3D confocal microscopy with specialized fluorescent molecules designed to label the mucus itself. With this toolkit, the researchers could map the physical terrain of individual clumps of marine snow in three dimensions and watch how bacteria navigated that landscape in real time. The technical details of the imaging method are described in a companion paper published in Nature Communications. What the technique revealed was unexpected: marine snow is not a homogeneous blob of slime but a structured habitat, riddled with bulges, crevasses, and regions of dramatically different consistency. Mucus, Inman noted, has a surprising amount of structure at a scale smaller than bacteria—a physical microenvironment that scientists had never previously been able to examine directly. The finding effectively opens a new window onto a habitat that covers much of the living world.

That structure matters because it shapes the battle between sinking particles and the bacteria that consume them. Like snowflakes on land, no two marine snow particles are alike; each is assembled from a different combination of organic and inorganic molecules, and each differs in its gooeyness. Using their new imaging approach, the researchers determined that bacteria can colonize the less viscous zones near a particle’s surface far more easily than the denser, more viscous regions deeper inside. Bacteria, Inman explained, can latch onto the less viscous portions of each particle but cannot reach the more viscous regions until they break them down first. He offered a vivid analogy: it is like a worm in an apple, where the flesh is easy to penetrate but the core is tough to chew through. The outer layers of a marine snow particle are an open buffet; the interior is a fortress.

The consequences of that microbial siege are profound for the carbon cycle. In a series of laboratory experiments, the team found that more viscous marine snow takes significantly longer for bacteria to degrade and fragment. Every hour a particle resists consumption is another hour it spends sinking, and every additional meter of descent moves its carbon cargo further from the surface reservoir where it could quickly return to the atmosphere. Computer simulations extended the laboratory findings, suggesting that the more viscous a particle is, the deeper it will ultimately sink. When Inman and his colleagues translated those results into global terms, they estimated that high-viscosity marine snow can carry more than six times the carbon to the deep ocean compared with low-viscosity particles. How far marine snow can sink, Inman observed, is a surprisingly complicated problem, and the new work adds another—very gooey—piece to the puzzle.

The study is also notable for being the first time scientists have examined live bacteria interacting with the physical structure of marine snow, or with any mucus for that matter. That gap in knowledge was never trivial. Interactions between mucus and bacteria are critical to maintaining the health of life across the planet: coral reefs depend on their surface mucus layers to host protective microbial communities, kelp forests rely on similar partnerships, land plant root systems negotiate with bacteria through mucilaginous coatings, and animal and human digestive tracts function only because of the finely tuned relationships between gut mucus and resident microbes. A method that finally makes the muscoscape visible therefore has implications far beyond oceanography, offering researchers in medicine, microbiology, and ecology a new way to study how bacteria behave inside the slimy matrices they inhabit.

For ocean and climate scientists, the findings arrive at a moment of intense scrutiny of the biological carbon pump, the suite of processes by which marine life exports carbon from the surface ocean. Global carbon models have long struggled to reconcile the enormous annual flux of marine snow with the much smaller fraction that reaches long-term storage depths. Viscosity, the new work suggests, is a variable that has been largely absent from those calculations, yet it may help explain why some particles survive the gauntlet of degradation while others dissolve into remineralized carbon within the upper ocean. If the distribution of particle gooeyness varies across ocean regions, seasons, or warming-driven changes in plankton communities, then the efficiency of the ocean’s carbon sink may be far more sensitive to microbial ecology than current models assume. Incorporating viscosity into future estimates could sharpen predictions of how much atmospheric carbon the ocean will continue to absorb as the climate changes.

The research was supported in part by additional funding from the Simons Foundation and the Engineering and Physical Sciences Research Council, and it reflects a broader shift in marine science toward treating microscopic processes as the engines of planetary-scale phenomena. What began as an effort to image slime in a laboratory dish has ended up quantifying a property that helps determine the fate of gigatons of carbon each year. As Inman and his colleagues continue to explore the muscoscape, the message of the new study is clear: the ocean’s carbon future may depend, in no small part, on how sticky its snow turns out to be. In the deep sea, where the snow never stops falling, gooiness is not a curiosity—it is climate machinery.

Subject of Research: The role of marine snow viscosity in microbial degradation and deep-ocean carbon transport

Article Title: The gooier marine snow is, the better it is at transporting carbon, new study finds

Article References: The gooier marine snow is, the better it is at transporting carbon, new study finds. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: marine snow, carbon cycle, ocean carbon sink, viscosity, bacteria, mucus, Scripps Institution of Oceanography, biological carbon pump, confocal microscopy, deep ocean, biogeochemistry, Science journal

The dataset, published in the journal Earth System Science Data, compiles observations made between 2008 and 2018 by the Underwater Vision Profiler 5, an underwater camera system that photographs particles and plankton as it descends through the sea. In total, the archive spans 3,114 vertical profiles collected across all major ocean basins, drawn from 62 separate research projects and validated by 71 different human annotators. Every image has been sorted into one of 33 standardized categories, from copepods and colonial radiolarians to salps, siphonophores, and amorphous detritus, and each object comes with 42 morphological measurements describing its size, shape, brightness, and texture.

The instrument behind the dataset is itself a triumph of ocean engineering. The UVP5 images roughly one liter of water per flash at frequencies of 5 to 20 hertz and can operate down to 6,000 meters, typically mounted on the rosette of water samplers deployed from research vessels. Because it photographs organisms in place, it avoids the crushing, tangling, and outright destruction that plankton nets inflict on fragile creatures like gelatinous zooplankton and delicate rhizarian protists. All particles larger than about 100 micrometers are counted and sized automatically, while the larger and more recognizable objects, those exceeding roughly 600 micrometers in the high-definition version and one millimeter in the standard-definition version, are cropped into individual vignettes for expert classification.

Perhaps the most striking finding is how lopsided the ocean’s visible particle world really is. Detritus, the non-living organic matter that includes marine snow, fecal pellets, and aggregates of dead organisms, accounts for 90.5 percent of all imaged objects and fully 95 percent of the total biovolume. Among living organisms, copepods, the tiny crustaceans that graze on phytoplankton, dominate the counts, followed by colonies of the nitrogen-fixing cyanobacterium Trichodesmium. Gelatinous filter-feeders and carnivores appear far less frequently but contribute disproportionately to total biomass because of their large body sizes, a reminder that abundance and ecological importance are very different things in the open ocean.

The spatial patterns captured in the archive tell a story about ocean fertility. Plankton and detritus concentrations peak in eastern boundary current systems, particularly the upwelling zones off California and Senegal, where nutrient-rich deep water fertilizes surface productivity, reaching up to five plankton and sixty detritus particles per cubic meter in the top 100 meters. The lowest concentrations occur in the centers of the great subtropical gyres, notably the South Pacific and Indian Ocean, where nutrient-starved waters support sparse communities. Concentrations decline steadily with depth, mirroring the rain of organic material from the sunlit surface toward the abyss.

Assembling a globally consistent dataset from a decade of work by dozens of independent teams posed formidable technical challenges. Different annotators classify images at different taxonomic depths, and human perception is vulnerable to fatigue, boredom, and bias toward familiar groups. To counter this, the team merged more than 250 original categories into 33 broad groups that could be applied consistently across all projects, each backed by at least 50 validated images. Quality control was rigorous: 200 images from each category, and ten percent of all detritus images, were independently re-reviewed, yielding error rates below eight percent for taxonomic classification and under 0.1 percent contamination of the detritus category by living plankton.

The researchers also confronted subtle instrumental differences. The standard-definition and high-definition versions of the UVP5 use different cameras, which changes the smallest objects each can resolve and alters morphological measurements such as grey levels. The two versions were inter-calibrated using concurrent particle size spectra measured in natural conditions, so sizes remain comparable between instruments, but the team cautions users that brightness and opacity measurements may not be directly comparable. One individual instrument, serial number 000, famously used during the Tara Oceans expeditions of 2009 to 2012, produced feature distributions that differ from all other units, and the authors provide a quantile-to-quantile transformation in the R programming language so users can correct for this before pooling data.

The scientific payoff could be enormous. Because plankton and detritus drive the biological carbon pump, the mechanism by which the ocean sequesters atmospheric carbon dioxide in its depths, models of climate and biogeochemistry depend critically on accurate data about these organisms and particles. Recent studies have shown that zooplankton grazing is one of the largest sources of uncertainty in marine carbon cycling within the latest generation of climate models, and that the morphology of marine snow particles strongly influences how fast they sink and how much carbon reaches the deep sea. A globally consistent, image-based record of both living plankton and sinking detritus gives modelers exactly the kind of ground truth they have lacked.

The dataset is also poised to become a training goldmine for artificial intelligence. Machine learning has already transformed plankton image analysis, with random forest classifiers and convolutional neural networks predicting object identities that humans then validate. The seven million detritus images in this release, most of which have never been sorted by morphology, represent an ideal challenge for next-generation algorithms, and the authors explicitly hope the community will use the archive to develop new classification tools. Successors of the UVP5, including the UVP6 now deployed on autonomous floats and ships, are generating roughly 5,000 new profiles and 87 million images in just the last four years, and embedded AI classifiers will be essential for platforms like biogeochemical Argo floats that are never recovered from the sea.

For all its scope, the archive has honest gaps. Two-thirds of the profiles come from the northern hemisphere, the equatorial band between 30 degrees south and 30 degrees north is best represented, and the Southern Ocean and deep sea remain conspicuously undersampled. Small sampled volumes make rare taxa easy to miss, and apparent zeros in the data may reflect detection limits rather than true absence. The authors frame this first release as a starting point rather than a finished product: roughly 10,000 additional profiles await full validation, and future work will focus on subdividing the sprawling detritus category into morphotypes such as fecal pellets, whose sinking behavior differs in ways that matter for carbon flux calculations. In an era when climate change is rapidly reshaping ocean temperature, acidity, and circulation, a shared, standardized, and openly available window into the ocean’s microscopic engine room could not have arrived at a better time.

Subject of Research: A global standardized image database of plankton and detritus from Underwater Vision Profiler 5 deployments

Article Title: A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5

Article References: Nocera, A. C., Stemmann, L., Babin, M., Biard, T., Coustenoble, J., Carlotti, F., Coppola, L., Courchet, L., Drago, L., Elineau, A., Guidi, L., Hauss, H., Jalabert, L., Karp-Boss, L., Kiko, R., Laget, M., Lombard, F., McDonnell, A., Merland, C., … Irisson, J.-O. (2026). A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5. Earth System Science Data, 18(10), 7345-7366. https://doi.org/10.5194/essd-18-7345-2026

Image Credits: AI Generated

DOI: 10.5194/essd-18-7345-2026

Keywords: plankton, detritus, marine snow, Underwater Vision Profiler, ocean imaging, carbon cycle, zooplankton, machine learning, biogeochemistry, ocean observation, copepods, Earth System Science Data

Melissa Omand, a research professor at the URI Graduate School of Oceanography, is leading the project, which is funded by a grant of nearly 700,000 dollars from the U.S. National Science Foundation. The three-year effort is scheduled to begin in January 2027 and run through December 2029. Working with collaborators Meg Estapa and Chaofan Chen at the University of Maine, Omand and her team will investigate whether machine learning models can predict the chemical composition of marine snow from characteristics that are visible in underwater images, such as particle size, shape, and transparency. If the approach succeeds, it could transform the way oceanographers interpret the enormous volumes of imagery their instruments already collect, turning pictures of drifting particles into quantitative estimates of what those particles are made of.

The stakes are considerable. Marine snow sits at the heart of the biological carbon pump, the set of processes by which the ocean sequesters carbon dioxide from the atmosphere. When phytoplankton die or are consumed, their remains aggregate into particles dense enough to sink. As Omand explains, these particles are rich in carbon and other nutrients, provide a key food source for organisms living in the deep ocean, and play an important role in marine carbon sequestration. The rate at which marine snow sinks, and the rate at which it is decomposed or consumed on the way down, determines how much carbon is locked away in the deep ocean and for how long. Better measurements of particle composition could therefore sharpen estimates of how the ocean will respond to a warming climate.

Underwater cameras have been part of the oceanographer’s toolkit for decades, and modern instruments can capture images of thousands of individual particles in a single deployment. Cameras mounted on autonomous floats and profilers can now document particle distributions across entire ocean basins. What the images cannot generally reveal, however, is chemistry. A particle that appears as a translucent blob in a photograph might be a nutrient-rich aggregate of organic matter, a mineral ballast fragment, or a piece of microplastic. Distinguishing among these possibilities has traditionally required collecting physical samples, filtering them, and analyzing them in laboratories, a slow and expensive process that limits how much of the ocean can be characterized.

The labor involved in manual image analysis is not trivial. Estapa noted that one of her graduate students spent months classifying particles and identifying what appeared in underwater images. That kind of effort, repeated across the field, represents a bottleneck at precisely the moment when imaging technology is generating more data than human analysts can process. The researchers hope that AI can automate much of this classification work, allowing scientists to collect and interpret more information while spending more of their time on scientific analysis and discovery. Crucially, the team also plans to develop models in ways that help scientists understand how the models reach their conclusions, addressing a common criticism of machine learning approaches in the sciences.

The foundation of the project is a carefully constructed database that pairs images with ground truth. Omand’s role in the first year will center on collecting marine snow images and matching physical samples across a broad range of ocean environments, spanning coastal waters off Ghana, the equatorial Atlantic, the New England shelf, and the California current system. Through collaborations with the Monterey Bay Aquarium Research Institute and the University of Ghana, the team already collected samples from two of these sites during the summer of 2026. The resulting database will link each image to information about the geochemical properties of the corresponding particles, the amount of microplastic present, and the location where the samples were gathered.

Once that database is assembled, the researchers will use data from six major oceanographic field campaigns to test whether groups of particles visible in images can accurately predict their chemical composition. The logic is that particle appearance is not random. Aggregates formed from diatom blooms look different from fecal pellets produced by zooplankton, and mineral-rich particles scatter light differently from soft organic ones. If those visual signatures correlate reliably with chemistry, a trained model could estimate carbon content, nutrient loading, or plastic contamination directly from photographs, without waiting for laboratory analysis. Validating such predictions across diverse ocean regions, from productive coastal shelves to the oligotrophic open ocean, will be the critical test of whether the method generalizes.

One of the most socially resonant applications involves plastic pollution. By identifying and quantifying plastic particles alongside naturally occurring marine snow, the researchers hope to better understand how plastic debris moves from surface waters into the deep ocean and through marine food webs. Microplastics have been found in the deepest ocean trenches and in the tissues of animals at every level of the marine food chain, yet the pathways by which they travel downward remain poorly quantified. If sinking aggregates are a major transport mechanism, as several studies suggest, then an AI system capable of flagging plastic-containing particles in routine imagery could produce basin-scale maps of plastic export that no sampling program could match.

The same tools could illuminate the natural cycling of carbon and nutrients through the ocean. As Omand puts it, gaining detailed insights into marine snow particles, their associated communities, and the links to their environment will allow scientists to better predict the movement of carbon and nutrients in the ocean and the impact that changes may have on marine life. That predictive capacity matters at a time when ocean warming, acidification, and shifting plankton communities are altering the efficiency of the biological pump in ways that global climate models struggle to capture. Particle-level observations, scaled up through automated analysis, could provide the empirical grounding those models lack.

For Omand, the project also represents a broader shift in how ocean science handles data. Research vessels, autonomous floats, and seafloor observatories now generate imagery faster than any laboratory can interpret it, and the techniques developed for marine snow could extend well beyond oceanography, to any scientific field that relies on complex images that are difficult and time-consuming to interpret. In that sense, the humble flecks drifting through the deep ocean may end up teaching researchers far from the sea a lesson about extracting knowledge from pictures. The snowfall that carries the ocean’s carbon downward may, with the help of machines that learn to read it, carry scientific understanding upward.

Subject of Research: Using artificial intelligence to determine the chemical composition of sinking marine snow particles from underwater imagery

Article Title: Reading the ocean’s snowflakes: How AI decodes marine snow

Article References: Reading the ocean’s snowflakes: How AI decodes marine snow. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: marine snow, artificial intelligence, carbon sequestration, microplastics, oceanography, biological carbon pump, underwater imaging, machine learning, National Science Foundation, University of Rhode Island, deep ocean, nutrient cycling