A molecular snapshot in time: eRNA recovers similar diversity but captures species turnover more rapidly than eDNA across an acid-base gradient
Bibliographic record
Abstract
Major ongoing declines in global biodiversity necessitate biomonitoring strategies that enable precise estimates of community diversity on a fine spatial and temporal scale. While environmental DNA (eDNA) has been established as a powerful tool for biodiversity assessment, studies investigating the comparative performance of environmental RNA (eRNA) are limited. Here, we performed eDNA/eRNA metabarcoding of zooplankton communities in outdoor freshwater mesocosms subject to a dynamic range of pH conditions. We comparatively assessed i ) the sensitivity of eRNA metabarcoding relative to eDNA and traditional survey methods in capturing zooplankton diversity, ii ) the influence of pH on eDNA/eRNA detectability, and iii ) the propensity of eRNA to capture contemporary biological assemblages (i.e., rapid species turnover) with high spatial and temporal acuity. Zooplankton richness was similar amongst eDNA/eRNA metabarcoding and traditional survey methods; however, the composition of zooplankton communities detected was more analogous between eDNA and eRNA metabarcoding than with traditional methods. Both eDNA and eRNA captured similar ZOTU richness and frequency of false negative detections (irrespective of site-specific pH); however, eRNA captured species turnover more rapidly than eDNA. Collectively, our findings suggest that i ) relative to traditional methods, eDNA and eRNA metabarcoding may provide users with complementary rather than congruent estimates of biodiversity, ii ) eDNA and eRNA provide comparable estimates of species richness irrespective of site-specific pH conditions, and iii ) eRNA is able to capture short-term community responses with higher spatial and temporal acuity than eDNA. Overall, our findings support the use of eRNA for characterizing contemporary biodiversity in complex and dynamic aquatic environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".