One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Seasonal phytoplankton and geochemical shifts in the subsurface chlorophyll maximum layer of a dimictic ferruginous lake.

Subsurface chlorophyll maxima layers (SCML) are ubiquitous features of stratified aquatic systems. Availability of the micronutrient iron is known to influence marine SCML, but iron has not been explored in detail as a factor in the development of freshwater SCML. This study investigates the relationship between dissolved iron and the SCML within the dimictic, ferruginous lake Grosses Heiliges Meer in northern Germany. The occurrence of the SCML under nonferruginous conditions in the spring and ferruginous conditions in the fall are context to explore temporal changes in the phytoplankton community and indicators of primary productivity. Results indicate that despite more abundant chlorophyll in the spring, the SCML sits below a likely primary productivity maximum within the epilimnion, inferred based on colocated dissolved oxygen, δ13 CDIC , and pH maxima. The peak amount of chlorophyll in the SCML is lower in the fall than in the spring, but in the fall the SCML is colocated with elevated dissolved iron concentrations and a local δ13 CDIC maximum. Cyanobacteria and Chlorophyta have elevated abundances within the SCML in the fall. Further investigation of the relationship of iron to primary productivity within ferruginous SCML may help to understand the environmental controls on primary productivity in past ferruginous oceans.

Improved detection of mcyA genes and their phylogenetic origins in harmful algal blooms.

Microcystins, a group of cyanotoxins produced by cyanobacterial strains, have become a significant microbial hazard to human and animal health due to increases in the frequency and intensity of cyanobacterial harmful algal blooms (CyanoHABs). Many studies have explored the correlation between microcystin concentrations and abundances of toxin-producing genes (e.g., mcyA genes) measured using quantitative PCR, and discrepancies between toxin concentrations and gene abundances are often observed. In this study, the results show that these discrepancies are at least partially due to primer sets that do not capture the phylogenetic diversity of naturally present toxin-producers. We designed three novel primer gene probes based on known mcyA genes to improve the detection and quantification of these genes in environmental samples. These primers were shown to improve the identification of mcyA genes compared to previously published primers in freshwater metagenomes, cyanobacterial isolates, and lake water samples. Unlike previously published primers, our primer sets could selectively amplify and resolve Microcystis, Anabaena, and Planktothrix mcyA genes. In lake water samples, abundance estimations of mcyA genes were found to correlate strongly with microcystin concentrations. Based on our results, these primers offer significant improvements over previously published probes to accurately identify and quantify mcyA genes in the environment. There is an increasing need to develop models based on microbial information and environmental factors to predict CyanoHABs, and improved primers will play an important role in aiding monitoring efforts to collect reliable and consistent data on toxicity risks.