Comparative analysis of zooplankton diversity in freshwaters: What can we gain from metagenomic analysis?
Bibliographic record
Abstract
Abstract Molecular genetic approaches applied to environmental DNA have great potential for biodiversity research and ecosystem monitoring. A metagenome is produced via shotgun sequencing of DNA collected directly from the environment and represents a sample of genetic information from all organisms captured in an environmental sample. Metagenomes have been primarily used to study bacteria and archaea, but promising reports focusing on metazoan diversity are emerging. However, methodological uncertainties remain, and studies are required to validate the power and the limitations of such an approach when applied to macro‐eukaryotes. Here, we analyzed water sample metagenomes to estimate zooplankton diversity in 22 freshwater lakes across eastern Canada. We tested the coherence of data based on field samples collected at the same time: 1) morphologically identified zooplankton specimens and 2) molecular genetic data derived from shotgun sequencing of environmental DNA for which we applied two different bioinformatic workflows: a whole metagenome mapping approach and a small subunit (SSU) rRNA gene prediction approach. We further evaluated diversity trends emerging from each dataset in relation to major environmental gradients. We found a significant correlation between the relative abundance of zooplankton families identified based on SSU rRNA gene prediction and morphology. However, differences in congruence between metagenomes and morphological identifications were detected when varied bioinformatic approaches were applied to the presence–absence data. This study presents one of the first diversity assessments of a group of aquatic metazoans using metagenomes and validates the coherence of the community composition derived from genomic and classical species surveys. Overall, our results suggest that metagenomics has the potential to be further developed to describe metazoan biodiversity in aquatic ecosystems, and to advance this area we provide key recommendations for workflow improvement.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".