Floodwater impact on Galveston Bay phytoplankton taxonomy, pigment composition and photo-physiological state following Hurricane Harvey from field and ocean color (Sentinel-3A OLCI) observations

Phytoplankton taxonomy, pigment composition and photo-physiological state were studied in Galveston Bay (GB), Texas (USA), following the extreme flooding associated with Hurricane Harvey (25–29 August 2017) using field and satellite ocean color observations. The percentage of chlorophyll a (Chl a) in different phytoplankton groups was determined from a semi-analytical IOP (inherent optical property) inversion algorithm. The IOP inversion algorithm revealed the dominance of freshwater species (diatom, cyanobacteria and green algae) in the bay following the hurricane passage (29 September 2017) under low salinity conditions associated with the discharge of floodwaters into GB. Two months after the hurricane (29–30 October 2017), under more seasonal salinity conditions, the phytoplankton community transitioned to an increase in small-sized groups such as haptophytes and prochlorophytes. Sentinel-3A Ocean and Land Colour Instrument (OLCI)-derived Chl a obtained using a red ∕ NIR (near-infrared) band ratio algorithm for the turbid estuarine waters was highly correlated (R2>0.90) to the (high-performance liquid chromatography) HPLC-derived Chl a. Long-term observations of OLCI-derived Chl a (August 2016–December 2017) in GB revealed that hurricane-induced Chl a declined to background mean state in late October 2017. A non-negative least squares (NNLS) inversion model was then applied to OLCI-derived Chl a maps of GB to investigate spatiotemporal variations of phytoplankton diagnostic pigments pre- and post-hurricane; results appeared consistent with extracted phytoplankton taxonomic composition derived from the IOP inversion algorithm and microplankton pictures obtained from an Imaging FlowCytobot (IFCB). OLCI-derived diagnostic pigment distributions also exhibited good agreement with HPLC measurements during both surveys, with R2 ranging from 0.40 for diatoxanthin to 0.96 for Chl a. Environmental factors (e.g., floodwaters) combined with phytoplankton taxonomy also strongly modulated phytoplankton physiology in the bay as indicated by measurements of photosynthetic parameters with a fluorescence induction and relaxation (FIRe) system. Phytoplankton in well-mixed waters (mid-bay area) exhibited maximum PSII photochemical efficiency (Fv∕Fm) and a low effective absorption cross section (σPSII), while the areas adjacent to the shelf (likely nutrient-limited) showed low Fv∕Fm and elevated σPSII values. Overall, the approach using field and ocean color data combined with inversion models allowed, for the first time, an assessment of phytoplankton response to a large hurricane-related floodwater perturbation in a turbid estuarine environment based on its taxonomy, pigment composition and physiological state.

Floodwater Impact on Galveston Bay Phytoplankton Taxonomy, Pigment Composition and Photo-Physiological State following Hurricane Harvey from Field and Ocean Color (Sentinel-3A OLCI) Observations

Phytoplankton taxonomy, pigment composition and photo-physiological state were studied in Galveston Bay (GB), Texas (USA) following the extreme flooding associated with Hurricane Harvey (August 25–29, 2017) using field and satellite ocean color observations. Percentage of chlorophyll a (Chl a) in different phytoplankton groups were determined from a semi-analytical IOP (inherent optical property) inversion algorithm. The IOP inversion algorithm revealed the dominance of freshwater species (cyanobacteria and green algae) in the bay following the hurricane passage (September 29, 2017) under low salinity conditions associated with the discharge of floodwaters into GB; 2 months after the hurricane (October 29–30, 2017), under more seasonal salinity conditions, the phytoplankton community transitioned to an increase in small sized groups such as haptophyte and prochlorophyte. Sentinel-3A OLCI-derived Chl a obtained using a red/NIR band ratio algorithm for the turbid estuarine waters was highly correlated (R2 > 0.90) to HPLC-derived Chl a concentrations. A Non-Negative Least Square (NNLS) inversion model was then applied to OLCI-derived Chl a maps of GB to obtain spatiotemporal distributions of phytoplankton diagnostic pigments; results appeared consistent with extracted phytoplankton taxonomic composition derived from the IOP inversion algorithm. OLCI-derived diagnostic pigment distributions also exhibited good agreement with HPLC measurements, with mean R2 ranging from 0.39 for violaxanthin to 0.98 for Chl a. Environmental factors (e.g. floodwaters) combined with phytoplankton taxonomy also strongly modulated phytoplankton physiology in the bay as indicated by measurements of photosynthetic parameters with a Fluorescence Induction and Relaxation (FIRe) system. Phytoplankton in well-mixed waters (mid-bay area) exhibited maximum PSII photochemical efficiency (FV/FM) and low effective absorption cross section (δPSII), while the areas adjacent to the shelf (likely nutrient-limited) showed low FV/FM and elevated values. Overall, the approach using field and ocean color data combined with inversion models allowed, for the first time, an assessment of phytoplankton response to a large hurricane-related floodwater perturbation in a turbid estuarine environment based on its taxonomy, pigment composition and physiological state.

Improved estimates of phytoplankton community composition based on in situ spectral fluorescence: use of ordination and field-derived norm spectra for the bbe FluoroProbe

The use of spectral fluorometers for assessing phytoplankton concentrations and taxonomic composition in aquatic environments is increasingly common. However, the accuracy of such assessments suffers because the necessary norm spectra (spectral fingerprints) are derived using selected taxa and laboratory conditions that may not adequately represent the taxa and environmental conditions in the study area. Ordination analysis of raw fluorescence data has been proposed as a better means of interpreting spectral fluorescence data. We applied nonmetric multidimensional scaling and cluster analysis to raw in situ fluorescence data from Sturgeon Bay, a small, mesotrophic embayment of Georgian Bay (Lake Huron) to obtain system-specific norm spectra for the bbe FluoroProbe. The revised spectra gave improved estimates of phytoplankton taxonomy (root mean square error of 10% versus 14%) and of dissolved organic carbon and chlorophyll a concentrations. While promising, this method should be further explored in other systems with different and (or) weaker gradients in phytoplankton biomass and taxonomic composition.