Evaluating the performance of DNA metabarcoding for assessment of zooplankton communities in Western Lake Superior using multiple markers
Bibliographic record
Abstract
For DNA metabarcoding to attain its potential as a community assessment tool, we need to better understand its performance versus traditional morphological identification and work to address any remaining performance gaps in incorporating DNA metabarcoding into community assessments. Using fragments of the 18S nuclear and 16S mitochondrial rRNA genes and two fragments of the mitochondrial COI marker, we examined the use of DNA metabarcoding and traditional morphological identification for understanding the diversity and composition of crustacean zooplankton at 42 sites across western Lake Superior. We identified 51 zooplankton taxa (genus or species, depending on the finest resolution of the taxon across all identification methods), of which 17 were identified using only morphological traits, 13 using only DNA and 21 using both methods. The taxa found using only DNA metabarcoding included four species and one genus-level identification not previously known to occur in Lake Superior, the presence of which still needs to be confirmed. A substantial portion of taxa that were identified to genus or species by morphological identification, but not identified using DNA metabarcoding, had zero ("no record") or ≤ 2 ("underrepresented records") reference barcodes in the BOLD or NCBI databases (63% for COI, 80% for 16S, 74% for 18S). The two COI marker fragments identified the most genus- and species-level taxa, whereas 18S was the only marker whose family-level percent sequence abundance patterns showed high correlation to composition patterns from morphological identification, based on a NMDS analysis of Bray-Curtis similarities. Multiple replicates were collected at a subset of sites and an occupancy analysis was performed, which indicated that rare taxa were more likely to be detected using DNA metabarcoding than traditional morphology. Our results support that DNA metabarcoding can augment morphological identification for estimating zooplankton diversity and composition of zooplankton over space and time, but may require use of multiple markers. Further addition of taxa to reference DNA databases will improve our ability to use DNA metabarcoding to identify zooplankton and other invertebrates in aquatic surveys.
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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 | 0.002 | 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 | 0.000 |
| 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".