The environmental biophysics of microalgal migration in snow

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?