Sporadic diurnal fluctuations of cyanobacterial populations in oligotrophic temperate systems can prevent accurate characterization of change and risk in aquatic systems.

Cyanobacteria increasingly threaten recreational water use and drinking water resources globally. They require dynamic monitoring to account for variability in their distribution arising from diel cycles associated with oscillatory vertical migration. While this has been discussed in marine and eutrophic freshwater contexts, reports of diurnal vertical migration of cyanobacteria in oligotrophic freshwater lakes are scant. Typical monitoring protocols do not reflect these dynamics and frequently focus only on surface water sampling approaches, and either ignore sampling time or recommend large midday timeframes (e.g., 10AM-3PM), thereby preventing accurate characterization of cyanobacterial community dynamics. To evaluate the impact of diurnal migrations and water column stratification on cyanobacterial abundance and composition, communities were characterized in a shallow well-mixed lake interconnected to a thermally stratified lake in the Turkey Lakes Watershed (Ontario, Canada) using amplicon sequencing of the 16S rRNA gene across a multi-time point sampling series in 2018 and 2022. This work showed that cyanobacteria are present in oligotrophic lakes and their community structure varies (i) diurnally, (ii) across the depth of the water column, (iii) interannually within the same lake and (iv) between different lakes that are closely interconnected within the same watershed. It underscored the need for integrating multi-timepoint, multi-depth discrete sampling guidance into lake and reservoir monitoring programs to describe cyanobacteria community dynamics and signal change to inform risk management associated with the potential for cyanotoxin production. Ignoring variability in cyanobacterial community dynamics (such as that reported herein) and reducing sample numbers can lead to a false sense of security and missed opportunities to identify and mitigate changes in trophic status and associated risks such as toxin or taste and odor production, especially in sensitive, oligotrophic systems.

Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing.

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities.

Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample's library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

Evaluating the performance of DNA metabarcoding for assessment of zooplankton communities in Western Lake Superior using multiple markers.

For DNA metabarcoding to attain its potential as a community assessment tool, we need to better understand its performance versus traditional morphological identification and work to address any remaining performance gaps in incorporating DNA metabarcoding into community assessments. Using fragments of the 18S nuclear and 16S mitochondrial rRNA genes and two fragments of the mitochondrial COI marker, we examined the use of DNA metabarcoding and traditional morphological identification for understanding the diversity and composition of crustacean zooplankton at 42 sites across western Lake Superior. We identified 51 zooplankton taxa (genus or species, depending on the finest resolution of the taxon across all identification methods), of which 17 were identified using only morphological traits, 13 using only DNA and 21 using both methods. The taxa found using only DNA metabarcoding included four species and one genus-level identification not previously known to occur in Lake Superior, the presence of which still needs to be confirmed. A substantial portion of taxa that were identified to genus or species by morphological identification, but not identified using DNA metabarcoding, had zero ("no record") or ≤ 2 ("underrepresented records") reference barcodes in the BOLD or NCBI databases (63% for COI, 80% for 16S, 74% for 18S). The two COI marker fragments identified the most genus- and species-level taxa, whereas 18S was the only marker whose family-level percent sequence abundance patterns showed high correlation to composition patterns from morphological identification, based on a NMDS analysis of Bray-Curtis similarities. Multiple replicates were collected at a subset of sites and an occupancy analysis was performed, which indicated that rare taxa were more likely to be detected using DNA metabarcoding than traditional morphology. Our results support that DNA metabarcoding can augment morphological identification for estimating zooplankton diversity and composition of zooplankton over space and time, but may require use of multiple markers. Further addition of taxa to reference DNA databases will improve our ability to use DNA metabarcoding to identify zooplankton and other invertebrates in aquatic surveys.