Sedimentary <scp>DNA</scp> Metabarcoding and Indigenous Knowledge Reconstruct Natural and Anthropogenic Disturbances to a Freshwater Lake in the Oil Sands Region of Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Sedimentary DNA (sedDNA), a form of environmental DNA (eDNA) shed by aquatic organisms and preserved in sediment, is crucial for reconstructing historical community compositions in aquatic ecosystems. In Cowpar Lake (Dene name: Doghostú), Alberta, a significant landslide event in the early 1940s CE impacted the lake's geochemistry and fish populations, as documented by Indigenous Knowledge from the Chipewyan Prairie First Nation and corroborated by targeted fish sedDNA analyses. The present study used 18S rRNA and cytochrome oxidase I (COI) genes for DNA metabarcoding of a sediment core from Cowpar Lake to assess the effect of the documented landslide and to reconstruct the historical community composition of eukaryotic functional trophic groups, including photoautotrophs, mixotrophs, parasites, and consumers. Between 1948 and 1956 CE, a notable shift in community composition occurred, with a decline in the alpha diversity of eukaryotic amplicon sequence variants. The increased primary productivity and terrestrial organic input post‐1950 is correlated with an increased diversity of phototrophs and mixotrophs, suggesting potential algal blooms. While parasite diversity remained stable, consumer diversity declined, likely due to increased microbial respiration of organic matter, reducing oxygen levels and making the lake less hospitable for consumers like whitefish, which eventually disappeared in the lake. The reconstructed eukaryotic community profiles from sedDNA were consistent with Indigenous Knowledge of natural changes around the lake. The present study highlights the potential of braiding sedDNA data with Indigenous Knowledge to reconstruct long‐term changes in aquatic communities, offering high‐resolution baseline data for environmental monitoring and a deeper understanding of how freshwater systems respond to natural and human‐induced impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".