Microcystin concentrations and congener composition in relation to environmental variables across 440 north-temperate and boreal lakes
Bibliographic record
Abstract
Understanding the environmental conditions and taxa that promote the occurrence of cyanobacterial toxins is imperative for effective management of lake ecosystems. Herein, we modeled total microcystin presence and concentrations with a broad suite of environmental predictors and cyanobacteria community data collected across 440 Canadian lakes using standardized methods. We also conducted a focused analysis targeting 14 microcystin congeners across 190 lakes, to examine how abiotic and biotic factors influence their relative proportions. Microcystins were detected in 30 % of lakes, with the highest total concentrations occurring in the most eutrophic lakes located in ecozones of central Canada. The two most commonly detected congeners were MC-LR (61 % of lakes) and MC-LA (37 % of lakes), while 11 others were detected more sporadically across waterbodies. Congener diversity peaked in central Canada where cyanobacteria biomass was highest. Using a zero-altered hurdle model, the probability of detecting microcystin was best explained by increasing Microcystis biomass, Daphnia and cyclopoid biomass, soluble reactive phosphorus, pH and wind. Microcystin concentrations increased with the biomass of Microcystis and other less dominant cyanobacteria taxa, as well as total phosphorus, cyclopoid copepod biomass, dissolved inorganic carbon and water temperature. Collectively, these models accounted for 34 % and 70 % of the variability, respectively. Based on a multiple factor analysis of microcystin congeners, cyanobacteria community data, environmental and zooplankton data, we found that the relative abundance of most congeners varied according to trophic state and were related to a combination of cyanobacteria genera biomasses and environmental variables.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".