DNA metabarcoding captures temporal and vertical dynamics of mesozooplankton communities
Bibliographic record
Abstract
Abstract In this study, we evaluated how well DNA metabarcoding of environmental samples captures changes in marine mesozooplankton community composition to optimize the use of sequencing data for studying seasonal dynamics. Although DNA metabarcoding is increasingly used to monitor the distribution of marine communities, there is a lack of standardized methods, and it remains uncertain to what extent the DNA data reflects patterns of community dynamics observed by other methods. Zooplankton net samples were collected every second week throughout 2017 in the northern Salish Sea, British Columbia. We compared metabarcoding of two genetic markers (18S targeting eukaryotes and cytochrome oxidase I targeting invertebrates) with microscopic assessments of the zooplankton collected. We also evaluated how data transformation using relative abundance, presence/absence, and the eDNA-index, affects the linearity between the morphological and genetic methods. Despite low taxonomic agreement between DNA metabarcoding and microscopy, we found most biomass dominating genera to be well represented. Using the eDNA-index, we found a generally good congruence between the seasonal cycles observed with microscopy and DNA, and that discrete water samples analyzed with DNA metabarcoding can provide information on the vertical distributions of mesozooplankton genera. We conclude by presenting guidelines for future studies that aim to use DNA to study marine zooplankton community dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".