Long-term trends in aquatic communities of mesotrophic lakes along a land-use gradient in eastern Canada
Bibliographic record
Abstract
Increased human development since the Industrial Era has intensified toxic cyanobacterial blooms, which pose significant risks to public health and can cause detrimental effects on lake ecosystems. Mesotrophic lakes are particularly sensitive to nutrient shifts, making them critical systems for understanding bloom dynamics. Sedimentary DNA from paleolimnological archives provides valuable insights into community structure by situating contemporary data within the context of historical dynamics. We analyzed sediment cores from six mesotrophic lakes in Quebec and Ontario (Canada) that span a modern human land-use gradient. Cores were analyzed using sedimentary DNA techniques targeting specific cyanobacteria marker genes, including quantitative (q)PCR and 16S rRNA gene sequencing. Cladoceran assemblages from the same cores provided complementary food web data. We detected widespread increases in cyanobacteria abundance across all lakes, with a higher magnitude of increase in lakes located in high intensity land-use catchments. Bloom-forming orders (Nostocales, Chroococcales, Oscillatoriales) increased in relative abundance in some lakes, often replacing unicellular Synechococcales, which tended to dominate less-impacted systems. These cyanobacterial shifts often coincided with compositional changes in cladoceran assemblages, favoring small-bodied and ecologically flexible taxa such as Bosmina and Chydorus brevilabris. Overall, the analyses of paleogenetic records demonstrate that climate change, land-use intensity, and lake-specific traits have driven century-scale cyanobacterial increases and food-web shifts in mesotrophic lakes, with impacts on both bloom dynamics and cladoceran communities. Such long-term analyses are essential for revealing how interacting human and climatic pressures reshape aquatic ecosystems and guiding more effective management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".