Does wetland restoration create an ecological trap for migrating Brown trout smolts?
Bibliographic record
Abstract
Restoring wetlands is often used by management to boost ecosystem services like improving downstream water quality, but it may create ecological traps for migrating salmonids by increasing migration time and predation rates, potentially compromising self‐sustaining populations. In River Gudenaa, Denmark, the wild Brown trout ( Salmo trutta ) population has declined over the past one to two decades, and it remains unclear whether this decrease is linked to higher mortality due to restored wetlands in the river's lower reaches. This study investigated the progression rates and survival of migrating wild Brown trout smolts through River Gudenaa and Randers Fjord before and after the wetland restoration using acoustic telemetry. In 2020 and 2021, 150 smolts were tagged and released, and their movements and survival were compared with those of 61 smolts tagged and released before the restoration, in 2003 and 2005. Smolt progression rates were significantly slower in the river and fjord after the restoration, with the greatest reduction in the river. Despite slower progression rates, restoration did not impact survival, suggesting the wetlands did not act as an ecological trap for smolts. However, it remains unknown whether the slower migration had carryover effects on sea survival by increasing energy expenditure and delaying sea arrival. The retention of a main river channel, with a directed albeit slower flow, likely kept smolts from venturing into the adjacent wetland lakes, where predation may be higher. By incorporating measures that support migrating fish, wetland restoration can remain a valuable management tool to secure ecosystem services while sustaining fish populations.
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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 | 0.001 | 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 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".