Impact of <scp>CO<sub>2</sub></scp>‐Induced Aquatic Acidification on Environmental <scp>DNA</scp> and <scp>RNA</scp> Shedding and Persistence
Bibliographic record
Abstract
ABSTRACT Anticipated future increases in CO 2 levels are predicted to have a diverse array of lethal and non‐lethal effects on the marine ecosystem. While there has been extensive research on the physiological impacts of ocean acidification on marine species, our understanding of how increasing levels of carbon dioxide affect the shedding and decay of environmental DNA and RNA (eDNA/eRNA) in marine habitats is limited. This may impede the effective adoption of environmental nucleic acid–based molecular tools for monitoring marine biodiversity and detecting rare or invasive species. In the present study, we conducted mesocosm experiments to determine the shedding and decay rate constants of eDNA and eRNA in M. gigas ( Magallana [ Crassostrea ] gigas ) using mitochondrially encoded tRNA leucine 1 ( mt‐tl1 ) marker at various partial pressures of CO 2 in seawater. To our knowledge, this is the first study manipulating seawater pH using CO 2 . We developed a sensitive and specific quantitative PCR‐based assay to detect M. gigas eDNA and eRNA. Higher CO 2 levels increased shedding rates, indicating greater organism stress and biological effects on oysters. Additionally, increased CO 2 accelerates DNA and RNA decay, suggesting that ocean acidification may impact the reliability of eDNA‐based biodiversity monitoring. Furthermore, eRNA displayed lower steady‐state concentrations and a shorter persistence time in comparison to eDNA, as is consistent with known biochemical properties of the molecules. These findings are presented in the context of previous work that adjusted pH through acid–base adjustment and temperature and highlight the importance of considering ocean acidification caused by differing CO 2 levels when using molecular tools for marine conservation and fisheries management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".