TY - BOOK
T1 - Hydrodynamic modelling and fluorescent spectral methods for characterising the spatial distribution of phytoplankton
AU - Alexander, Ryan
PY - 2012
Y1 - 2012
N2 - Identifying structure in aquatic environments and showing the relationship to phytoplankton diversity is challenging because it is di cult 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 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.
AB - Identifying structure in aquatic environments and showing the relationship to phytoplankton diversity is challenging because it is di cult 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 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.
KW - Spectral fluorescence
KW - Phytoplankton niches
KW - Phytoplankton patchiness
KW - Principal component analysis
M3 - Doctoral Thesis
ER -