Covariance of Phytoplankton, Bacteria, and Zooplankton Communities Within Microcystis Blooms in San Francisco Estuary

Machine learning predicts the growth of cyanobacterial genera in river systems and reveals their different environmental responses

Cyanobacterial blooms are a common and serious problem in global freshwater environments. However, the response mechanisms of various cyanobacterial genera to multiple nutrients and pollutants, as well as the factors driving their competitive dominance, remain unclear or controversial. The relative abundance and cell density of two dominant cyanobacterial genera (i.e., Cyanobium and Microcystis) in river ecosystems along a gradient of anthropogenic disturbance were predicted by random forest with post-interpretability based on physicochemical indices. Results showed that the optimized predictions all reached strong fitting with R2 > 0.75, and conventional water quality indices played a dominant role. One-dimensional and two-dimensional partial dependence plot (PDP) revealed that the responses of Cyanobium and Microcystis to nutrients and temperature were similar, but they showed differences in preferrable nutrient utilization and response to pollutants. Further prediction and PDP for the ratio of Cyanobium and Microcystis unveiled that their distinct responses to PAHs and SPAHs were crucial drivers for their competitive dominance over each other. This study presents a new way for analyzing the response of cyanobacterial genera to multiple environmental factors and their dominance relationships by interpretable machine learning, which is suitable for the identification and interpretation of high-dimensional nonlinear ecosystems with complex interactions.

Use of multiple sampling techniques for cyanobacteria and cyanotoxin monitoring in the Sacramento-San Joaquin Delta under different hydrologic regimes

Cyanobacteria harmful algal blooms (CHABs) are a growing water quality problem in the upper San Francisco Estuary (California), also known as the Sacramento-San Joaquin Delta. We conducted cyanobacteria and cyanotoxin monitoring from 2020 to 2023, which spanned California's driest consecutive 3-year period and one of the wettest years on record (2023). To assess the impact of CHABs over this range of hydrologic conditions, we monitored invasive Asian Clams (Corbicula fluminea) for microcystin contamination and used molecular tools (qPCR and sequencing) to characterize cyanobacteria in the water column. We also used solid phase adsorption toxin tracking (SPATT) samplers to track microcystins (MCs) and other cyanotoxins in 2023. During the drought years, record breaking MCs, in excess of 1000 μg/L, were documented in water grab samples and Asian clams also accumulated higher MCs relative to the wet year. However, MCs were present in Asian clams during the entire study period. SPATT's confirmed MC presence during wet 2023 and sequencing results corroborated the integrative sampler findings. Yet, no MC was detected in water grab samples at our primary sampling sites during the drought year of 2022 or the wet year of 2023. This highlights the importance of using multiple sampling modalities to provide a more accurate assessment of MC contamination, especially in large estuaries where traditional discrete monitoring can easily miss episodic and transient CHAB events.

Insights into cyanobacterial blooms through the lens of omics

Cyanobacteria are oxygen-producing photosynthetic bacteria that convert carbon dioxide into biomass upon exposure to sunlight. However, favorable conditions cause harmful cyanobacterial blooms (HCBs), which are the dense accumulation of biomass at the water surface or subsurface, posing threats to freshwater ecosystems and human health. Understanding the mechanisms underlying cyanobacterial bloom formation is crucial for effective management. In this regard, recent advancements in omics technologies have provided valuable insights into HCBs, which have raised expectations to develop more effective control methods in the near future. This literature review aims to present the genomic architecture, adaptive mechanisms, microbial interactions, and ecological impacts of HCBs through the lens of omics. Genomic analysis indicates that the genome plasticity of cyanobacteria has enabled their resilience and effective adaptation to environmental changes. Transcriptomic investigations have revealed that cyanobacteria use various strategies for adapting to environmental stress. Additionally, metagenomic and metatranscriptomic analyses have emphasized the significant role of the microbial community in regulating HCBs. Finally, we offer perspectives on potential opportunities for further research in this field.

Indole-3-acetic acid promotes growth in bloom-forming Microcystis via an antioxidant response

Interactions between bacteria and phytoplankton in the phycosphere facilitate and constrain biogeochemical cycling in aquatic ecosystems. Indole-3-acetic acid (IAA) is a bacterially produced chemical signal that promotes growth of phytoplankton and plants. Here, we explored the impact of IAA on bloom-forming cyanobacteria and their associated bacteria. Exposure to IAA and its precursor, tryptophan, resulted in a strong growth response in a bloom of the freshwater cyanobacterium, Microcystis. Metatranscriptome analysis revealed the induction of an antioxidant response in Microcystis upon exposure to IAA, potentially allowing populations to increase photosynthetic rate and overcome internally generated reactive oxygen. Our data reveal that co-occurring bacteria within the phycosphere microbiome exhibit a division of labor for supportive functions, such as nutrient mineralization and transport, vitamin synthesis, and reactive oxygen neutralization. These complex dynamics within the Microcystis phycosphere microbiome are an example of interactions within a microenvironment that can have ecosystem-scale consequences.

Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States

Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.