Snow algae are swimming through snowpacks in the Arctic and alpine regions, and no one knows exactly how they decide where to go. The project aims to answer two basic questions: what environmental cues—light, gravity, or flowing meltwater—steer these single-celled algae, and how their movement changes the snow itself. This matters because snow algae are a double-edged sword. They act as carbon sinks and cycle nutrients near penguin and seal colonies, but in large blooms they darken the snow surface, reducing its reflectivity. That accelerates snowmelt, which can raise local temperatures, worsen flooding, and contribute to sea level rise. The researchers will build synthetic snow slabs and collect field samples, then alter light intensity, gravity, and shear flow while filming algae at both macroscopic and microscopic scales. Machine learning models will track individual cells in three dimensions through the snow’s pore spaces. If successful, the work will produce a scalable, quantitative model of how algal contamination alters snowpack thermodynamics. This is fundamental science—it will not yield a product or policy tomorrow. But understanding the biophysics of algal migration could eventually improve predictions of snowmelt timing and magnitude in a warming climate, affecting water supply and flood risk for communities downstream of snow-covered mountains.
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Microalgal blooms and the dynamics of their propagation are well studied within many ecosystems, namely across marine and estuarine environments and substrates. The mechanics of microalgae species inhabiting snowpacks within polar, arctic and alpine environments are arguably understudied in comparison. Snow algae, particularly the species Chlamydomonas nivalis, a unicellular green algae, are found worldwide. Despite their widespread presence and global significance in carbon sequestration and snowmelt, an important feature of snow algae migration yet to be understood is the biophysics of their swimming within snowpacks. Snow algae colonies act as terrestrial carbon sinks, facilitate nutrient cycling - notably adjacent to penguin and seal colonies and play an impactful role in microbial communities. In contrast to their constructive attributes, in excess quantities they have been shown to heavily alter snow albedo, which can lead to snow melt, changes in atmospheric temperature, flooding and sea level rise. The project is driven by two main aims. The first is to identify how environmental cues bias the swimming of algae in snow and the second, to determine how the spatio-temporal distribution of algae alters the optical and thermodynamic properties of the snow it inhabits. Microalgal motion and snowpack thermodynamics within both synthetically generated snow slabs and field snow samples will be observed while altering environmental stimuli e.g., light intensity, gravitational stress and shear flow to find critical values. Video and image-based observations will be recorded, both macroscopically and microscopically. Specialised snow quality monitoring equipment will be used to assess thermodynamic changes to the snowpack, and computer vision machine learning models will be used to analyse the migration of algae across frames in the video and image-based data. Ideally, this will successfully identify algal swimming patterns in three dimensions amongst varying snow structures and quantify the impacts of algal contamination in snowpacks in a manner that is scalable and representative of real environmental scenarios under a changing climate. The project's two primary research questions are as follows: How do environmental cues (light, gravity, fluid flow) bias the swimming of algae in snow? and How does the spatio-temporal distribution of algae change the optical and thermodynamic properties of the snow?
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