Diatom identification including life cycle stages through morphological and texture descriptors

Diatoms are unicellular algae present almost wherever there is water. Diatom identification has many applications in different fields of study, such as ecology, forensic science, etc. In environmental studies, algae can be used as a natural water quality indicator. The diatom life cycle consists of the set of stages that pass through the successive generations of each species from the initial to the senescent cells. Life cycle modeling is a complex process since in general the distribution of the parameter vectors that represent the variations that occur in this process is non-linear and of high dimensionality. In this paper, we propose to characterize the diatom life cycle by the main features that change during the algae life cycle, mainly the contour shape and the texture. Elliptical Fourier Descriptors (EFD) are used to describe the diatom contour while phase congruency and Gabor filters describe the inner ornamentation of the algae. The proposed method has been tested with a small algae dataset (eight different classes and more than 50 samples per type) using supervised and non-supervised classification techniques obtaining accuracy results up to 99% and 98% respectively

Instrument and Method Development For Single-Cell Classification Using Fluorescence Imaging Multivariate Optical Computing

Multivariate optical computing (MOC) is an all-optical approach of predictive spectroscopy that utilizes multivariate calibration and spectral pattern recognition techniques while operating in a simple filter photometer instrument, removing the need for expensive instrumentation and post-processing of spectral data. This is accomplished with specially designed interference filters called multivariate optical elements (MOEs). MOC can provide analytical solutions for applications requiring low cost, rugged, and simple to operate instrumentation for use in remote and hazardous environments such as open ocean waters. These instrument specifications are central for developing a method for classifying phytoplankton in their natural environment. Phytoplankton are photosynthetic single cell algae and cyanobacteria that inhabit nearly all natural bodies of water The size and taxonomic composition of the phytoplankton community structure has global implications on carbon transport. This dissertation describes the development of a single streak imaging multivariate optical computing (SSIMOC) method for single-cell classification of phytoplankton. The design and fabrication of MOEs for phytoplankton classification, along with an imaging photometer constructed for the purpose of collected data images for MOC analysis, will be discussed. Results of data collected with the SSIMOC on cultured phytoplankton and coastal water collected near the Martha\u27s Vineyard Coastal Observatory will be shown

Diazotrophic cyanobacteria in planktonic food webs

Blooms of cyanobacteria are recurrent phenomena in coastal estuaries. Their maximum abundance coincides with the productive period of zooplankton and pelagic fish. Experimental studies indicate that diazotrophic, i.e. dinitrogen (N2)-fixing cyanobacterial (taxonomic order Nostocales) blooms affect zooplankton, as well as other phytoplankton. We used multidecadal monitoring data from one archipelago station (1992–2013) and ten open sea stations (1979–2013) in the Baltic Sea to explore the potential bottom-up connections between diazotrophic and non-diazotrophic cyanobacteria and phyto- and zooplankton in natural plankton communities. Random forest regression, combined with linear regression analysis showed that the biomass of cyanobacteria (both diazotrophic and non-diazotrophic) was barely connected to any of the phytoplankton and zooplankton variables examined. Instead, physico-chemical variables (salinity, temperature, total phosphorus), as well as spatial and temporal variability seemed to have more significant connections to both phytoplankton and zooplankton variables. Zooplankton variables were also connected to the biomass of phytoplankton groups other than cyanobacteria (such as chrysophytes, cryptophytes and prymnesiophytes), and phytoplankton variables had connections with the biomass of different zooplankton groups, especially copepods. Overall, negative relationships between cyanobacteria and other plankton taxa were scarcer than expected based on previous experimental studies.​​​​​​​</ul

Diazotrophic cyanobacteria in planktonic food webs

Blooms of cyanobacteria are recurrent phenomena in coastal estuaries. Their maximum abundance coincides with the productive period of zooplankton and pelagic fish. Experimental studies indicate that diazotrophic, i.e. dinitrogen (N2)-fixing cyanobacterial (taxonomic order Nostocales) blooms affect zooplankton, as well as other phytoplankton. We used multidecadal monitoring data from one archipelago station (1992–2013) and ten open sea stations (1979–2013) in the Baltic Sea to explore the potential bottom-up connections between diazotrophic and non-diazotrophic cyanobacteria and phyto- and zooplankton in natural plankton communities. Random forest regression, combined with linear regression analysis showed that the biomass of cyanobacteria (both diazotrophic and non-diazotrophic) was barely connected to any of the phytoplankton and zooplankton variables examined. Instead, physico-chemical variables (salinity, temperature, total phosphorus), as well as spatial and temporal variability seemed to have more significant connections to both phytoplankton and zooplankton variables. Zooplankton variables were also connected to the biomass of phytoplankton groups other than cyanobacteria (such as chrysophytes, cryptophytes and prymnesiophytes), and phytoplankton variables had connections with the biomass of different zooplankton groups, especially copepods. Overall, negative relationships between cyanobacteria and other plankton taxa were scarcer than expected based on previous experimental studies