Spatio-temporal connectivity of the aquatic microbiome associated with cyanobacterial blooms along a Great Lake riverine-lacustrine continuum
Bibliographic record
Abstract
Lake Erie is subject to recurring events of cyanobacterial harmful algal blooms (cHABs), but measures of nutrients and total phytoplankton biomass seem to be poor predictors of cHABs when taken individually. A more integrated approach at the watershed scale may improve our understanding of the conditions that lead to bloom formation, such as assessing the physico-chemical and biological factors that influence the lake microbial community, as well as identifying the linkages between Lake Erie and the surrounding watershed. Within the scope of the Government of Canada’s Genomics Research and Development Initiative (GRDI) Ecobiomics project, we used high-throughput sequencing of the 16S rRNA gene to characterize the spatio-temporal variability of the aquatic microbiome in the Thames River–Lake St. Clair-Detroit River–Lake Erie aquatic corridor. We found that the aquatic microbiome was structured along the flow path and influenced mainly by higher nutrient concentrations in the Thames River, and higher temperature and pH downstream in Lake St. Clair and Lake Erie. The same dominant bacterial phyla were detected along the water continuum, changing only in relative abundance. At finer taxonomical level, however, there was a clear shift in the cyanobacterial community, with Planktothrix dominating in the Thames River and Microcystis and Synechococcus in Lake St. Clair and Lake Erie. Mantel correlations highlighted the importance of geographic distance in shaping the microbial community structure. The fact that a high proportion of microbial sequences found in the Western Basin of Lake Erie were also identified in the Thames River, indicated a high degree of connectivity and dispersal within the system, where mass effect induced by passive transport play an important role in microbial community assembly. Nevertheless, some cyanobacterial amplicon sequence variants (ASVs) related to Microcystis , representing less than 0.1% of relative abundance in the upstream Thames River, became dominant in Lake St. Clair and Erie, suggesting selection of those ASVs based on the lake conditions. Their extremely low relative abundances in the Thames suggest additional sources are likely to contribute to the rapid development of summer and fall blooms in the Western Basin of Lake Erie. Collectively, these results, which can be applied to other watersheds, improve our understanding of the factors influencing aquatic microbial community assembly and provide new perspectives on how to better understand the occurrence of cHABs in Lake Erie and elsewhere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".