Evaluating the congruence between <scp>DNA</scp>‐based and morphological taxonomic approaches in water and sediment trap samples: Analyses of a 36‐month time series from a temperate monomictic lake
Bibliographic record
Abstract
Abstract Paleolimnological studies are central for identifying long‐term changes, yet many studies rely on bioindicators that deposit detectable subfossils in sediments, such as diatoms and cladocerans. Emerging DNA‐based approaches are expanding the taxonomic diversity that can be investigated. However, as sedimentary DNA‐based approaches are expanding rapidly, calibration work is required to determine the advantages and limitations of these techniques. In this study, we assessed the congruence between morphological and DNA‐based approaches applied to sediment trap samples for diatoms and crustaceans using both intracellular and extracellular DNA. We also evaluated which taxa are deposited in sediment traps from the water column to identify potential paleolimnological bioindicators of environmental variations. Based on 18S rRNA gene amplicons, we developed and analyzed a micro‐eukaryotic, monthly time series that spanned 3 years and was comprised of paired water column and sediment trap samples from Cultus Lake, British Columbia, Canada. Comparisons of assemblages derived from our genetic and morphological analyses using RV coefficients revealed significant correlations for diatoms, but weaker correlations for crustaceans. Intracellular DNA reads correlated more strongly with diatom morphology, while extracellular DNA reads correlated more strongly with crustacean morphology. Additional analyses of amplicon sequence variants shared between water and sediment trap samples revealed a wide diversity of taxa to study in paleolimnology, including Ciliophora, Dinoflagellata, Chytridiomycota, Chrysophyceae, and Cryptophyceae. Partial RDAs identified significant environmental predictors of these shared assemblages. Overall, our study demonstrates the effectiveness of DNA‐based approaches to track community dynamics from sediment samples, an essential step for successful paleolimnological studies.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".