SEMINAR: Thesis Presentation:

Identifying structure in aquatic environments and showing the relationship to phytoplankton diversity is challenging because it is difficult to make direct measurements of all relevant variables at the necessary temporal and spatial scales. Two new approaches are demonstrated, which allow relationships between phytoplankton distribution and the aquatic environment to be better understood.

The first approach involved the use of numerical modelling to resolve structures in the aquatic environment at smaller spatial and temporal scales than traditional field sampling allows. A three-dimensional, coupled physical-biological numerical model (ELCOM-CAEDYM) was used to reconcile a range of different unsteady processes that influenced the spatial distribution of motile phytoplankton in a medium sized reservoir located in central Argentina. It was determined that physical processes (with some influence from phytoplankton migration) control the habitat of the motile phytoplankton rather than biological/chemical gradients. The results suggest that numerical models can be used to characterise the spatial habitat of other motile phytoplankton species in similar settings.

The second approach involved the use of fluorescence spectral measurements as a proxy indicator of phytoplankton diversity. As fluorescence spectra can be measured rapidly in situ, in principle, spectral measurements can be made at a resolution that should allow many scales of phytoplankton patchiness to be resolved. However, decoding the information contained within the spectral measurements presents a challenge. Therefore, a method based on principal component analysis (PCA) was developed for identifying patches of distinct fluorescent groupings of phytoplankton from in situ spectral data. A series of idealised spectral data sets were used to explain the conceptual basis of the approach. To demonstrate the method, a profiling multi-wavelength fluorometer was cast at numerous locations throughout Winam Gulf, Kenya.

Processing the spectral data with PCA revealed that linear combinations of four fundamental base spectra could explain almost all of the variation in the spectral measurements. Three of the base spectra were associated with spatially distinct patches of phytoplankton containing different species assemblages, while the fourth base spectrum was due to fluorescence of coloured dissolved organic matter (CDOM). Strong relationships were found between the gradients in spectral data and other environmental variables, which suggested several underlying explanations for the phytoplankton and CDOM patchiness. The PCA processing method has the capacity to summarise critical features contained with large spectral data sets and can facilitate better optimisation of traditional water sampling.

PS* This seminar is free and open to the public & no RSVP required.

****All Welcome****