In this paper we examine the potential role of light limitation in the regulation of phytoplankton standing crop in Florida's largest river, the St. Johns. We hypothesized that spatial and temporal patterns in standing crops of phytoplankton are strongly affected by variations in light availability in the mixed layer, particularly as they relate to river basin morphology and changes in water color, which reach high levels in the St. Johns River. This hypothesis was examined within the context of four principal research objectives: (1) Determination of the spatial and temporal patterns of phytoplankton standing crops and key environmental parameters related to nutrient concentration and light extinction, (2) Estimation of spatial and temporal patterns of mean light availability in the mixed-layer, (3) Examination of the correlations between phytoplankton standing crops and light availability, and (4) Evaluation of the relative role of different limiting factors on the regulation of phytoplankton standing crop. The results of this study revealed a relationship between standing crops and light availability. Twelve sampling sites along a 130 km reach of the river were sampled and analyzed for phytoplankton abundance, water chemistry, and light attenuation over a three year period. Our empirical results, along with the outcome of our efforts to model light availability for planktonic production were consistent with our original hypothesis. Temporal variations in color were strongly correlated to variability in phytoplankton standing crops. Spatial trends in standing crop were most readily explained through the effects of changing basin morphology and flushing rates. The results are discussed in the context of the River Continuum Concept and variations on this theme specific to blackwater ecosystems.

1. The effect of variability in rainfall on the potential for algal blooms was examined for the St Johns River in northeast Florida. Water chemistry and phytoplankton data were collected at selected sites monthly from 1993 through 2003. Information on rainfall and estimates of water turnover rates were used in the analyses of trends in phytoplankton biomass. 2. Major trends in rainfall and runoff within the lower St Johns River catchment over the 10-year study period were marked by both significant drought and flood periods. Autumn and winter rainfall patterns were strongly correlated with the range of Pacific sea surface temperature anomalies associated with El Niñ o events and La Niñ a periods. The effect of these major shifts in rainfall was evident in the strong relationship to replacement rates for water within the lower St Johns River. 3. The eutrophic status of the river was reflected in the high concentrations of nitrogen and phosphorus observed at all sampling sites, with total nitrogen concentrations up to 3100 lg L )1 and total phosphorus concentrations up to 180 lg L )1 . 4. While it is clear that the high phytoplankton biomass and frequent blooms that characterize the freshwater portions of the lower St Johns River are fundamentally based on nutrient status, the expression of that potential was strongly correlated to water replacement rates, as revealed by the inverse relationship between phytoplankton biovolume increase and water turnover rate, with an R 2 of 0.80 for the major bloom season. The sensitivity of algal blooms to rainfall patterns over the 10-year study period suggest that longer-term temporal and spatial shifts in rainfall, such as multi-decadal cycles and the global-warming phenomenon, will also influence the frequency and intensity of algal blooms.

Harmful algal blooms are a growing human and environmental health hazard globally. Eco-physiological diversity of the cyanobacteria genera that make up these blooms creates challenges for water managers tasked with controlling the intensity and frequency of blooms, particularly of harmful taxa (e.g., toxin producers, N fixers). Compounding these challenges is the ongoing debate over the efficacy of nutrient management strategies (phosphorus-only versus nitrogen and phosphorus), which increases decision-making uncertainty. To improve our understanding of how different cyanobacteria respond to nutrient levels and other biophysical factors, we analyzed a unique 17 year data set comprising monthly observations of cyanobacteria genera and zooplankton abundances, water quality, and flow in a bloom-impacted, subtropical, flow-through lake in Florida (United States). Using the Random Forests machine learning algorithm, an ensemble modeling approach, we characterized and quantified relationships among environmental conditions and five dominant cyanobacteria genera. Results highlighted nonlinear relationships and critical thresholds between cyanobacteria genera and environmental covariates, the potential for hydrology and temperature to limit the efficacy of cyanobacteria bloom management actions, and the importance of a dual nutrient management strategy for reducing bloom risk in the long term.

No abstract

No abstract