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1. Uptake of Phytoplankton-Derived Carbon and Cobalamins by Novel Acidobacteria Genera in Microcystis Blooms Inferred from Metagenomic and Metatranscriptomic Evidence

   https://doi.org/10.1128/aem.01803-21  

   Smith, Derek J.; Kharbush, Jenan J.; Kersten, Roland D.; Dick, Gregory J. (July 2022, Applied and Environmental Microbiology)

   Glass, Jennifer B. (Ed.)

   ABSTRACT Interactions between bacteria and phytoplankton can influence primary production, community composition, and algal bloom development. However, these interactions are poorly described for many consortia, particularly for freshwater bloom-forming cyanobacteria. Here, we assessed the gene content and expression of two uncultivated Acidobacteria from Lake Erie Microcystis blooms. These organisms were targeted because they were previously identified as important catalase producers in Microcystis blooms, suggesting that they protect Microcystis from H 2 O 2 . Metatranscriptomics revealed that both Acidobacteria transcribed genes for uptake of organic compounds that are known cyanobacterial products and exudates, including lactate, glycolate, amino acids, peptides, and cobalamins. Expressed genes for amino acid metabolism and peptide transport and degradation suggest that use of amino acids and peptides by Acidobacteria may regenerate nitrogen for cyanobacteria and other organisms. The Acidobacteria genomes lacked genes for biosynthesis of cobalamins but expressed genes for its transport and remodeling. This indicates that the Acidobacteria obtained cobalamins externally, potentially from Microcystis , which has a complete gene repertoire for pseudocobalamin biosynthesis; expressed them in field samples; and produced pseudocobalamin in axenic culture. Both Acidobacteria were detected in Microcystis blooms worldwide. Together, the data support the hypotheses that uncultured and previously unidentified Acidobacteria taxa exchange metabolites with phytoplankton during harmful cyanobacterial blooms and influence nitrogen available to phytoplankton. Thus, novel Acidobacteria may play a role in cyanobacterial physiology and bloom development. IMPORTANCE Interactions between heterotrophic bacteria and phytoplankton influence competition and successions between phytoplankton taxa, thereby influencing ecosystem-wide processes such as carbon cycling and algal bloom development. The cyanobacterium Microcystis forms harmful blooms in freshwaters worldwide and grows in buoyant colonies that harbor other bacteria in their phycospheres. Bacteria in the phycosphere and in the surrounding community likely influence Microcystis physiology and ecology and thus the development of freshwater harmful cyanobacterial blooms. However, the impacts and mechanisms of interaction between bacteria and Microcystis are not fully understood. This study explores the mechanisms of interaction between Microcystis and uncultured members of its phycosphere in situ with population genome resolution to investigate the cooccurrence of Microcystis and freshwater Acidobacteria in blooms worldwide. 

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2. Dual phosphorus and nitrogen nutrient reduction will be more effective than a phosphorus‐only reduction in mitigating diatom and cyanobacterial blooms in Lake Erie, USA –Canada

   https://doi.org/10.1002/lno.12719  

   Paerl, Hans W; Chaffin, Justin D; Cheshire, Jack H; Plaas, Haley E; Barnard, Malcolm A; Goerlitz, Lillian B; Braddy, Jeremy S; Sabo, Alexandrea; Nelson, Leah M; Yue, Lindsay (November 2024, Limnology and Oceanography)

   Abstract Lake Erie, USA–Canada, plays an important ecological and socioeconomic role but has suffered from chronic eutrophication. In particular, western Lake Erie (WLE) is the site of harmful algal blooms (HABs) which are suspected of being driven by excessive nutrient (phosphorus (P) and nitrogen (N)) inputs. During 2022 and 2023, in situ nutrient dilution and addition bioassays were conducted at a WLE bloom‐impacted location to investigate whether a nutrient reduction regime would be effective in limiting phytoplankton growth during the June diatom‐dominated spring blooms and August cyanobacteria‐dominated summer blooms. The primary objectives of this experiment were to (1) Determine if a proposed 40% P‐alone reduction would effectively reduce phytoplankton growth and mitigate blooms and (2) assess whether reductions in both P and N are more effective in controlling phytoplankton biomass than exclusive reductions in either N or P. Samples were analyzed for nutrient concentrations and growth rate responses for specific algal groups, utilizing diagnostic (for major algal groups) photopigments. Results indicated that although both 20% and 40% dilutions led to lower phytoplankton biomass and growth rates, 40% reductions were more effective. Our results support the USA–Canada Great Lakes Water Quality Agreement recommendation of a 40% P reduction, but also indicate that a parallel reduction of N input by 40% would be most effective in controlling bloom magnitudes. Overall, our findings underscore the recommendation that a year‐round dual N and P 40% reduction is needed for long‐term control of eutrophication and algal blooms, including cyanobacteria and diatoms, in Lake Erie. 

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3. Harmful algal blooms in inland waters

   https://doi.org/10.1038/s43017-024-00578-2  

   Feng, Lian; Wang, Ying; Hou, Xuejiao; Qin, Boqiang; Kutser, Tiit; Qu, Fan; Chen, Nengwang; Paerl, Hans W; Zheng, Chunmiao (September 2024, Nature Reviews Earth & Environment)

   Harmful algal blooms can produce toxins that pose threats to aquatic ecosystems and human health. In this Review, we outline the global trends in harmful algal bloom occurrence and explore the drivers, future trajectories and potential mitigation strategies. Globally, harmful algal bloom occurrence has risen since the 1980s, including a 44% increase from the 2000s to 2010s, especially in Asia and Africa. Enhanced nutrient pollution owing to urbanization, wastewater discharge and agricultural expansion are key drivers of these increases. In contrast, changes have been less substantial in high-income regions such as North America, Europe and Oceania, where policies to mitigate nutrient pollution have stabilized bloom occurrences since the 1970s. However, since the 1990s, climate warming and legacy nutrient pollution have driven a resurgence in toxic algal blooms in some US and European lakes, highlighting the inherent challenges in mitigating harmful blooms in a warming climate. Indeed, advancing research on harmful algal bloom dynamics and projections largely depends on effectively using data from multiple sources to understand environmental interactions and enhance modelling techniques. Integrated monitoring networks across various spatiotemporal scales and data-sharing frameworks are essential for improving harmful algal bloom forecasting and mitigation. 

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4. Data from: Physiological and ecological drivers of early spring blooms of a coastal phytoplankter

   https://doi.org/10.5061/DRYAD.JM8S7  

   Hunter-Cevera, Kristen R; Neubert, Michael G; Olson, Robert J; Solow, Andrew R; Shalapyonok, Alexi; Sosik, Heidi M (January 2017, Dryad)

   Climate affects the timing and magnitude of phytoplankton blooms that fuel marine food webs and influence global biogeochemical cycles. Changes in bloom timing have been detected in some cases, but the underlying mechanisms remain elusive, contributing to uncertainty in long-term predictions of climate change impacts. Here we describe a 13-year hourly time series from the New England shelf of data on the coastal phytoplankter Synechococcus, during which the timing of its spring bloom varied by 4 weeks. We show that multiyear trends are due to temperature-induced changes in cell division rate, with earlier blooms driven by warmer spring water temperatures. Synechococcus loss rates shift in tandem with division rates, suggesting a balance between growth and loss that has persisted despite phenological shifts and environmental change. 

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5. Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

   https://doi.org/10.3390/jmse11081608  

   Lin, Junan; Liu, Qianqian; Song, Yang; Liu, Jiting; Yin, Yixue; Hall, Nathan S. (August 2023, Journal of Marine Science and Engineering)

   The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems. 

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