Habitat losses and anthropogenic barriers as a cause of population decline for American eel (Anguilla rostrata) in the St. Lawrence watershed, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.American eel (Anguilla rostrata) experienced a major decline over past decades. Although fisheries-related mortality could be accurately estimated, there is no global assessment of non-exploitation anthropogenic impacts. Within the St. Lawrence watershed, which contributes about 19% of the freshwater runoff in the species’ range, we found that more than 8411 dams were erected whose 151 are equipped with hydroelectricity facilities. These obstacles prevent free access to an estimated 12 140 km2 of freshwater habitat. Data analysis from three tributaries in the St. Lawrence watershed allowed evaluating annual productivity and production for female from freshwater habitats. Calculation leads to an estimated escapement of 87.1 kg/km2/year. The extension of this value to the area above barriers in the historic distribution range, suggests that the annual productivity losses could have been as much as 836 500 eels, mostly large fecund females. In comparison, the estuarine fishery at the outlet of the St. Lawrence watershed, with an historical annual peak capture of 340,000 silver eels (1920-2004) represented a loss for the population equivalent to only 40% of the potential production above dams. By preventing upstream passage, dams are clearly a major cause of abundance decline. Thus, re-opening access should be a priority. Simulations of re-opening some access could potentially contribute for 737 000 spawners per year. This net contribution could however only be obtained with the addition of efficient protection devices against mortalities caused by turbines. These actions appear as being among the most efficient to stop the decline of the species.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".