期刊
HARMFUL ALGAE
卷 108, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.hal.2021.102080
关键词
Cyanobacteria; Eutrophication; Environmental monitoring; International collaboration; Microcystis; Microcystin
资金
- National Oceanic and Atmospheric Administration's National Centers for Coastal Ocean Science [NA17NOS4780186]
- National Institutes of Health (NIH) [1P01ES028939-01]
- National Science Foundation [OCE-1840715]
- NIH [P30 ES020957]
- Natural Sciences and Engineering Research Council of Canada
- Fisheries and Oceans for the operation of the R.V. Cisco
- U.S. Environmental Protection Agency (EPA)
- NOAA Great Lake Environmental Research Laboratory
- Cooperative Institute for Great Lakes Research
Remote sensing allows for high-resolution monitoring of cyanobacterial bloom biomass in large lakes, but grab samples are still needed to monitor cyanobacterial toxins. Intensive grab sampling events were conducted in Lake Erie to estimate the total mass of microcystin during harmful algal blooms, revealing spatial heterogeneity and challenges for predictions. Models need to consider this spatial variability when forecasting microcystins.
Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sam-pling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive HABs Grabs of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km(2) and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.
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