Using environmental DNA to monitor the reintroduction success of the Rhine sculpin ( <i>Cottus rhenanus</i> ) in a restored stream
Bibliographic record
Abstract
As a consequence of the strong human impact on freshwater ecosystems, restoration measures are increasingly applied to restore and maintain their good ecological status. The ecological status of freshwaters can be inferred by assessing the presence of indicator species, such as the Rhine sculpin ( Cottus rhenanus ). However, traditional methods of monitoring fish, such as electrofishing, are often challenging and invasive. To augment or even replace the traditional fish monitoring approach, the analysis of environmental DNA (eDNA) has recently been proposed as an alternative, sensitive approach. The present study employed this modern approach to monitor the Rhine sculpin, a species that has been reintroduced into a recently restored stream within the Emscher catchment in Germany, in order to validate the success of the restorations. We monitored the dispersal of the Rhine sculpin using replicated 12S end-point PCR eDNA surveillance at a fine spatial and temporal scale to investigate the applicability of analyzing eDNA for freshwater ecosystem monitoring. We also performed traditional electrofishing in one instance to compare visual and eDNA-based assessments. We could track the dispersal of the Rhine sculpin and showed a higher dispersal potential of the species than we assumed. Furthermore, the eDNA analysis showed higher sensitivity for detecting the species than traditional electrofishing, although false negative results occurred at early reintroduction stages. Our results show that analyzing eDNA is capable of validating and tracking ecological reintroductions and contribute to the assessment and modelling of ecological status of streams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".