Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions.

In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie.

Interannual variability of cyanobacterial blooms in Lake Erie.

After a 20-year absence, severe cyanobacterial blooms have returned to Lake Erie in the last decade, in spite of negligible change in the annual load of total phosphorus (TP). Medium-spectral Resolution Imaging Spectrometer (MERIS) imagery was used to quantify intensity of the cyanobacterial bloom for each year from 2002 to 2011. The blooms peaked in August or later, yet correlate to discharge (Q) and TP loads only for March through June. The influence of the spring TP load appears to have started in the late 1990 s, after Dreissenid mussels colonized the lake, as hindcasts prior to 1998 are inconsistent with the observed blooms. The total spring Q or TP load appears sufficient to predict bloom magnitude, permitting a seasonal forecast prior to the start of the bloom.

AN "ENVIRO-INFORMATIC" ASSESSMENT OF SAGINAW BAY (LAKE HURON, USA) PHYTOPLANKTON: DATA-DRIVEN CHARACTERIZATION AND MODELING OF MICROCYSTIS (CYANOPHYTA)(1).

Phytoplankton and Microcystis aeruginosa (Kütz.) Kütz. biovolumes were characterized and modeled, respectively, with regard to hydrological and meteorological variables during zebra mussel invasion in Saginaw Bay (1990-1996). Total phytoplankton and Microcystis biomass within the inner bay were one and one-half and six times greater, respectively, than those of the outer bay. Following mussel invasion, mean total biomass in the inner bay decreased 84% but then returned to its approximate initial value. Microcystis was not present in the bay during 1990 and 1991 and thereafter occurred at/in 52% of sample sites/dates with the greatest biomass occurring in 1994-1996 and within months having water temperatures >19°C. With an overall relative biomass of 0.03 ± 0.01 (mean + SE), Microcystis had, at best, a marginal impact upon holistic compositional dynamics. Dynamics of the centric diatom Cyclotella ocellata Pant. and large pennate diatoms dominated compositional dissimilarities both inter- and intra-annually. The environmental variables that corresponded with phytoplankton distributions were similar for the inner and outer bays, and together identified physical forcing and biotic utilization of nutrients as determinants of system-level biomass patterns. Nonparametric models explained 70%-85% of the variability in Microcystis biovolumes and identified maximal biomass to occur at total phosphorus (TP) concentrations ranging from 40 to 45 µg · L(-1) . From isometric projections depicting modeled Microcystis/environmental interactions, a TP concentration of <30 µg · L(-1) was identified as a desirable contemporary "target" for management efforts to ameliorate bloom potentials throughout mussel-impacted bay waters.

Microcystin concentrations and genetic diversity of Microcystis in the lower Great Lakes.

The resurgence of Microcystis blooms in the lower Great Lakes region is of great concern to public and ecosystem health due to the potential for these colonial cyanobacteria to produce hepatotoxic microcystins. A survey of Microcystis cell densities and microcystin concentrations during August 2004 showed particularly high concentrations of both cells and toxin in the nearshore regions of Saginaw Bay (Lake Huron) and western Lake Erie, often exceeding the World Health Organization's recommended drinking water limit of 1 microg L(-1). The dominant congener of microcystin in both basins was microcystin-LR (MC-LR), whereas the second most abundant congeners, accounting for up to 20-25% of the total microcystin concentrations, were MC-LA in Saginaw Bay and MC-RR in western Lake Erie. Multiplex PCR assays of Microcystis colonies isolated from these two regions showed that a much greater percentage of the Microcystis colonies from Saginaw Bay carried the mcyB gene necessary for microcystin production, in comparison with those from western Lake Erie. The mcyB genotypes sequenced separated into two distinct phylogenetic clusters, with Microcystis originating from Lake Erie predominantly in one branch and from Saginaw Bay present in both branches. These results indicate that the genetic composition of the bloom could impact the concentrations and congeners of microcystin produced and that the cell count methods currently being used to gauge public health threats posed by Microcystis blooms may not sufficiently assess actual bloom toxicity.

Simplified enrichment and identification of environmental peptide toxins using antibody-capture surfaces with subsequent mass spectrometry detection.

The development of a simplified assay for detection of congeners of the microcystin (MC) hepatotoxin is described that combines the extreme sensitivity of surface-enhanced laser desorption/ionization time-of-flight MS (SELDI TOF-MS) with the superior selectivity of immunoaffinity interactions. Using methods similar to those of conventional immunoassays, MC standards were captured and enriched on immunoreactive ProteinChips coated with an MC-antibody and analyzed by TOF-MS. Unlike with conventional immunoassays, individual congeners were resolved from mixed pools. Assay conditions were optimized for the quantification of MC from untreated raw pond water at concentrations as low as 0.025 microg L(-1), well below the public health relevant guideline of 1 microg L(-1).