The study, led by Elise Ryan of Western Washington University and senior author Alia Khan, a cryosphere biogeochemist now at the University of Colorado Boulder, is the first to draw a direct link between penguin guano and the different pigmented stages of the snow algae life cycle. Khan, who began the research while at Western Washington University, argues that this biological darkening effect has been a blind spot in polar science. Without accounting for the way wildlife nutrients fuel algal blooms, she says, scientists are likely underestimating how rapidly coastal snowpacks are melting and how quickly ice-free areas will spread across the continent. In a warming Antarctic, where snow algae habitats are already expanding along the coasts, that missing piece could meaningfully reshape projections of the peninsula’s future.

To understand why penguin poop matters, it helps to understand the strange biology of Antarctic snow algae. These microscopic organisms live in the top layers of coastal snowpack and can grow densely enough to visibly change the color of the snow itself. Their appearance is not random; it reflects their physiological state. When nutrients are abundant and conditions are favorable, the algae remain metabolically active, packing their cells with green pigments such as chlorophyll to maximize photosynthesis during the brief Antarctic summer. In effect, green snow is growing snow, with the algae racing to convert sunlight into biomass during a short window of liquid water and daylight.

When nutrients run scarce or conditions turn harsh, the algae switch strategies. Instead of growing, they hunker down, producing red, sunscreen-like pigments that shield their cells from intense solar radiation while putting growth on hold. This is the phenomenon popularly known as red snow, or watermelon snow, which has long been documented in polar and alpine environments. Both colored states darken the snow relative to its clean white baseline, but they do so to very different degrees. Red snow absorbs roughly 20 percent more energy from sunlight than clean snow, and that extra absorbed energy heats the snow surface and drives additional melt.

Green snow, however, is in a league of its own. Where nutrients fuel more green pigment and a higher overall density of algae cells, the snow absorbs twice the amount of extra energy compared with the red state, about 40 percent more than clean white snow. That doubled radiative forcing translates directly into a faster melt rate, because every additional unit of solar energy absorbed at the surface goes into warming and melting the snowpack rather than being reflected back to space. In this way, a nutrient subsidy from the marine food web can cascade into a physical feedback loop: more nutrients produce greener algae, greener algae absorb more sunlight, more absorbed sunlight melts more snow, and more melting creates the wet conditions that allow algae habitats to expand further.

Gathering the evidence for this chain required some unglamorous fieldwork. The team traveled by boat across the notoriously rough seas of the Drake Passage to sample both red and green snow algae along the coast of the West Antarctic Peninsula, covering five degrees of latitude and visiting sites both with and without penguin colonies. Sampling near the colonies meant working up close with smelly bird droppings, using ceramic spoons to scoop algae-tinted snow into bags while penguins waddled through their own waste and tracked it across the snow. Khan noted that penguin poop can even visually resemble red snow algae in the field, making the work both stinky and occasionally confusing.

In the laboratory, the distinction became far clearer. Ryan and other members of Khan’s lab counted algae cells in each snow sample under a microscope and chemically extracted the pigments and nutrients each sample contained. The pattern that emerged was striking: wherever the algae cells carried predominantly green pigments, the surrounding snow contained far more phosphate, a critical nutrient found in guano, than samples where the algae displayed predominantly red, protective pigments. That correlation ties the nutritional status of the algae directly to the presence of wildlife-derived fertilizer, and it explains why blooms near penguin colonies tend to stay in their green, high-melt state rather than reverting to the redder dormant phase.

The finding, Khan explained, links the marine food web to snow ecology. After feasting on marine creatures at sea, penguins and seabirds such as skuas spread the resulting nutrients, including phosphate, over the coastal snowpack in the form of their droppings. The guano effectively acts as a fertilizer that fuels dense green snow algae blooms, connecting the productivity of the Southern Ocean to the energy balance of the snow surface above it. It is a vivid example of how biological processes and cryospheric physics interact in polar environments, and of how a colony of seabirds can influence the melt rate of the landscape around it simply by going about their daily routines.

The implications reach into how scientists model the future of Antarctica. As temperatures rise across the continent, snow algae habitats are already expanding along the coasts, darkening the surface and amplifying warming and snowmelt in a classic positive feedback. At the same time, penguins are shifting their ranges, waddling closer to the South Pole in response to changing environmental conditions. That movement creates the potential for guano-fertilized algae habitats to spread their melt-enhancing effects over a larger area than they occupy today. Yet current projections of Antarctic melting do not account for this phenomenon, because until now no study had demonstrated a direct connection between wildlife nutrient inputs and the pigmentation states that control how much energy the snow absorbs.

Khan’s team is the first to show that link, but the researchers caution that more data is needed before guano-fueled greening can be incorporated into snowmelt projections for the Antarctic Peninsula. Quantifying the effect across seasons, colonies, and latitudes will be essential to determine how much of the peninsula’s future ice-free expansion this biological darkening can explain. Still, the study makes clear that the missing component is significant: the interplay between wildlife, nutrients, and snow algae is, in Khan’s words, a critical missing component in projections of ice-free area expansion. If the green patches keep growing on the strength of penguin fertilizer, the map of Antarctica’s coastline may be redrawn faster than current models predict, one fertilized snowfield at a time.

Subject of Research: The role of penguin guano nutrients in driving green snow algae blooms and accelerated snowmelt on the West Antarctic Peninsula

Article Title: Penguin poop is melting Antarctic snow by keeping algae green

Article References: Penguin poop is melting Antarctic snow by keeping algae green. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Antarctica, snow algae, penguin guano, phosphate, snowmelt, Antarctic Peninsula, chlorophyll, albedo, cryosphere, biogeosciences, ice-free expansion, marine food web

Zooplankton occupy a pivotal position in marine ecosystems. As the principal consumers of phytoplankton, they convert microscopic plant production into animal biomass that can be passed to higher trophic levels, including commercially important fish stocks. They also play an outsized role in biogeochemical cycling, influencing the exchange of carbon dioxide between the atmosphere and the surface ocean and packaging organic carbon into fecal pellets that sink toward the deep sea. Any sustained change in their abundance therefore ripples outward, affecting nutrient cycling, water quality, fisheries yields, and the ocean’s capacity to sequester carbon. Yet despite their importance, zooplankton dynamics in the Indian Ocean have received far less attention than in other ocean basins, partly because quality-controlled, gridded biogeochemical data for the region have long been scarce.

To overcome this data gap, the team drew on the Coastal and Oceanic Plankton Ecology, Production, and Observation Database, known as COPEPOD, a global archive containing more than 400,000 records of copepods and other plankton taxa, with observations stretching back to around 1913. The biomass fields from the upper epipelagic layer, typically between the surface and 200 meters, were converted into carbon mass and merged into a single gridded product at one-degree horizontal resolution. The researchers then evaluated how well seven Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project, or CMIP6, could reproduce the observed patterns, integrating model zooplankton values down to 200 meters for the historical period 1975 to 2014 and for future scenarios running from 2061 to 2100.

The observed picture is strikingly asymmetric. Zooplankton concentration in the Arabian Sea averages about 15.6 milligrams of carbon per cubic meter, roughly 20 percent higher than in the Bay of Bengal, where the mean is about 13.0 milligrams per cubic meter. The difference stems from the fundamentally different hydrography of the two basins. The Arabian Sea experiences monsoon-driven upwelling, winter convective mixing, and high evaporation that produces dense, salty surface water which sinks and stirs the water column, all of which replenish nutrients in the sunlit layer. The Bay of Bengal, by contrast, is drenched by freshwater runoff from major rivers, which suppresses salinity, strengthens stratification, and blocks the upward transport of nutrient-rich subsurface water. Only near river mouths, where terrigenous nutrients locally offset the stratification, does the Bay of Bengal show elevated zooplankton concentrations.

The seasonal rhythm of zooplankton in the Arabian Sea is equally distinctive. Biomass peaks twice each year. The dominant peak arrives during the summer monsoon from July to September, when the strong southwesterly winds drive coastal and open-ocean upwelling, particularly along the western Arabian Sea, where the northward-flowing Somali Current lifts nutrient-laden deep water to the surface. A secondary peak occurs in winter from December to February, when the dry northeast monsoon cools the surface waters of the northern Arabian Sea, weakens thermal stratification, and allows convective mixing to fertilize the euphotic zone. Concentrations bottom out in April and May, when intense solar heating strengthens stratification and cuts off the vertical nutrient supply. The Bay of Bengal, in contrast, shows remarkably little seasonal variability, with a modest maximum in August and September.

Before projecting the future, the authors rigorously tested the models against observations. Statistical measures including correlation coefficients, root mean square errors, and skewness revealed that GFDL-ESM4, MPI-ESM1-2-HR, and IPSL-CM6A-LR performed best at capturing both the spatial pattern and the temporal variability of zooplankton over the Indian Ocean, with correlations between roughly 0.58 and 0.68, significant at the 99.9 percent level. Other models fared poorly: CanESM5 showed the worst performance, while CESM2 produced an abruptly high error of 9.06 milligrams of carbon per cubic meter. The team constructed a multi-model mean from the three best performers, a strategy that prioritizes spatial pattern fidelity over ensemble size. The authors also caution that converting modeled zooplankton biomass from nitrogen to carbon units introduces uncertainty, since plausible copepod carbon-to-nitrogen ratios alone can shift estimated biomass by 10 to 40 percent, though model bias generally explains the larger discrepancies.

The future projections are sobering. Under the moderate SSP2-4.5 scenario and the very high SSP5-8.5 scenario, zooplankton concentration declines across the entire basin, with regional reductions ranging from 5 to 40 percent. The steepest losses, exceeding 5 milligrams of carbon per cubic meter or roughly 40 percent, are projected for the western and central Arabian Sea under the high-warming pathway, while the southern Bay of Bengal is more affected than the north. When the researchers linearly detrended the datasets to remove long-term warming signals, the projected differences became statistically insignificant, confirming that greenhouse-gas-driven climate change, rather than natural variability, is the dominant cause of the decline. Notably, the projected zooplankton losses exceed the 5 to 20 percent declines in chlorophyll and net primary productivity reported in earlier CMIP5 and CMIP6 studies, hinting at a trophic amplification of climate impacts as warming effects cascade from primary producers to secondary consumers.

The mechanism behind the decline is a familiar one in ocean-climate science but is here documented with unusual regional detail. As the ocean warms, sea surface temperatures rise and sea surface salinity falls, and the upper layers of the Arabian Sea heat faster than the deeper waters. This sharpens the density contrast between surface and subsurface layers, strengthening vertical stratification and shoaling the mixed layer. A thinner, more isolated surface layer receives less nutrient-rich deep water through upwelling and entrainment, so phytoplankton growth slows and zooplankton, which depend on that grazing resource, decline in turn. Because zooplankton also mediate the biological carbon pump, reduced biomass could weaken the export of organic carbon to the deep ocean and increase remineralization in surface waters, with consequences for the ocean’s role in the global carbon budget.

Perhaps the most intriguing finding concerns the changing relationship between zooplankton and the great modes of natural climate variability, the El Niño-Southern Oscillation and the Indian Ocean Dipole. In the historical period, the El Niño-Southern Oscillation is significantly negatively correlated with zooplankton concentration over most of the Arabian Sea and the southern and central Bay of Bengal. Under future warming, however, that negative correlation over the Arabian Sea weakens almost to vanishing, suggesting that long-term stratification and warming trends will override interannual climate signals there. Process-based analysis supports this: ENSO-driven thermocline deepening and weakened wind stress curl anomalies suppress Ekman pumping and upwelling, cutting off the nutrient supply. In the Bay of Bengal and the equatorial Indian Ocean, the opposite occurs, with ENSO and the Indian Ocean Dipole projected to enhance zooplankton concentrations through thermocline shoaling and increased upwelling, potentially making those ecosystems more volatile from year to year.

The regional divergence carries real-world consequences. A more stable but steadily declining zooplankton base in the Arabian Sea, and more erratic swings in the Bay of Bengal, could translate into greater uncertainty in fish catches for the densely populated coastlines that border both basins, threatening food security across Indian Ocean rim countries. The authors acknowledge important limitations: CMIP6 models represent zooplankton as a single aggregated functional group, so shifts in community composition, body size, and trophic efficiency remain invisible to the analysis, even though warming is known to favor smaller, warm-adapted taxa that produce more slowly sinking fecal pellets and export less carbon. Coarse model resolution may also miss vertical redistribution of biomass through diel migration. Even so, the simulated decline in biomass is robust across models, and the study underscores an urgent need for sustained ocean monitoring, taxonomically resolved ecosystem models, and adaptation strategies to manage the changing marine ecosystems of the Indian Ocean.

Subject of Research: Upper-ocean zooplankton variability and projected climate-driven decline in the Indian Ocean using COPEPOD observations and CMIP6 model simulations

Article Title: Understanding upper ocean zooplankton variability in the Indian Ocean using observations and CMIP6 models

Article References: Understanding upper ocean zooplankton variability in the Indian Ocean using observations and CMIP6 models. (n.d.). https://doi.org/10.1007/s44289-026-00177-7

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00177-7

Keywords: zooplankton, Indian Ocean, Arabian Sea, Bay of Bengal, CMIP6, climate change, ocean stratification, upwelling, ENSO, Indian Ocean Dipole, marine food web, monsoon