<scp>eDNA</scp>‐based detection reveals invasion risks of a biofouling bivalve in the world's largest water diversion project
Bibliographic record
Abstract
Environmental DNA (eDNA) has increasingly been used to detect rare species (e.g., newly introduced nonindigenous species) in both terrestrial and aquatic ecosystems, often with distinct advantages over traditional methods. However, whether water eDNA signals can be used to inform invasion risks remains debatable owing to inherent uncertainties associated with the methods used and the varying conditions among study systems. Here, we sampled eDNA from canals of the central route of the South-to-North Water Diversion Project (hereafter SNWDP) in China to investigate eDNA distribution and efficacy to inform invasion risks in a unique lotic system. We first conducted a total of 16 monthly surveys in this system (two sites in the source reservoir and four sites in the main canal) to test if eDNA could be applied to detect an invasive, biofouling bivalve, the golden mussel Limnoperna fortunei. Second, we initiated a one-time survey in a sub-canal of the SNWDP using refined sampling (12 sites in ~22 km canal) and considered a few environmental predictors. We found that detection of target eDNA in the main canal was achieved up to 1100 km from the putative source population but was restricted to the warmer months (May-November). Detection probability exhibited a significant positive relationship with average daily minimum air temperature and with water temperature, consistent with the expected spawning season. eDNA concentration in the main canal generally fluctuated across months and sites and was generally higher in warmer months. Golden mussel eDNA concentration in the sub-canal decreased significantly with distance from the source and with increasing water temperature and became almost undetectable at ~22 km distance. Given the enormity of the SNWDP, golden mussels may eventually expand their distribution in the main canal, with established "bridgehead" populations facilitating further spread. Our findings suggest an elevated invasion risk of golden mussels in the SNWDP in warm months, highlighting the critical period for spread and, possibly, 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 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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".