Graduation Year


Document Type




Degree Granting Department

Marine Science

Major Professor

Thomas L. Hopkins, Ph.D.

Committee Member

Douglas C. Biggs, Ph.D.

Committee Member

Frank E. Müller-Karger, Ph.D.

Committee Member

Joseph J. Torres, Ph.D.

Committee Member

John J. Walsh, Ph.D.


Zooplankton, Gulf of Mexico, Machine learning, Sampling systems, Optics


Understanding the processes controlling the distribution and abundance of zooplankton has been a primary concern of oceanographers and has driven the development of numerous technologies to more accurately quantify these parameters. This study investigates the potential of a new plankton imaging sensor, the shadowed image particle profiling and evaluation recorder (SIPPER), that I helped develop at the University of South Florida, to address that concern. In the first chapter, results from the SIPPER are compared against concurrently sampling plankton nets and the optical plankton counter (OPC), the most widely used optical zooplankton sampling sensor in the field. It was found that plankton nets and the SIPPER sampled robust and hard-bodied zooplankton taxa similarly while nets significantly underestimated the abundance of fragile and gelatinous taxa imaged by the SIPPER such that nets might underestimate zooplankton biomass by greater than 50%. Similarly, it was determined that the OPC misses greater than a quarter of resolvable particles due to coincident counting and that it can not distinguish between zooplankton and other abundant suspended particles such as marine snow and Trichodesmium that are difficult to quantify with traditional sampling methods. Therefore the standard method of using net samples to ground truth OPC data should be reevaluated. In the second chapter, a new automated plankton classification system was utilized to see if it was possible to use machine learning methods to classify SIPPER-imaged plankton from a diverse subtropical assemblage on the West Florida Shelf and describe their distribution during a 24 hour period. Classification accuracy for this study was similar to that of other studies in less diverse environments and similar to what could be expected by a human expert for a complex dataset. Fragile plankton taxa such as larvaceans, hydromedusae, sarcodine protoctists and Trichodesmium were found at significantly higher concentrations than previously reported in the region and thus could play more important roles in WFS plankton dynamics. Most observed plankton classes were found to be randomly distributed at the fine scale (mm-100 m) and that greatest variability within plankton abundances would be encountered vertically rather than horizontally through the water column.