Using Environmental DNA to Monitor the Reintroduction Success of the Rhine Sculpin (Cottus rhenanus) in a Restored Stream
Bibliographic record
Abstract
Freshwaters belong to the most threatened ecosystems with some of the highest rates of species loss. To decrease these high extinction rates, ecological restorations are increasingly applied to restore and maintain the good ecological status of freshwaters. Their ecological status can be determined by assessing the presence of indicator species (e.g. certain fish species), which is called biomonitoring. However, traditional biomonitoring of fish, such as electrofishing, is often challenging and invasive. To augment traditional biomonitoring of fish, 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 (Cottus rhenanus), a fish species that has been reintroduced into a recently restored stream within the Emscher catchment in Germany, in order to validate the success of the applied restorations. We monitored the dispersal of the Rhine sculpin using replicated 12S end-point PCR eDNA surveillance at a fine spatial and temporal scale. In that way, we investigated if eDNA analysis can be applied for freshwater assessments. We also performed traditional electrofishing in one instance to validate our eDNA-based approach. We could track the dispersal of the Rhine sculpin and showed a higher dispersal potential of the species than we assumed. We validated the species’ dispersal across a potential dispersal barrier via eDNA detection and showed a steep increase of positive detections once the reintroduced population had established. In contrast to that, false negative eDNA results occurred at early reintroduction stages. Our results show that eDNA detection can be used to validate and monitor reintroductions and to contribute to the assessment and modelling of ecological status of streams.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".