Influence of dams on population persistence in Atlantic salmon (<i>Salmo</i> <i>salar</i>)
Bibliographic record
Abstract
Barriers to migration can negatively affect population persistence. To explore how dams can influence the viability of a diadromous fish, we developed an empirically based stochastic model to estimate per-capita population growth rate (r) and probability of population decline (Pr(r < 0)). Our simulations incorporated life-history parameters common for many populations of Atlantic salmon (Salmo salar L., 1758), particularly in the southern part of the species range. Additionally, we explored the influence of individuals that reproduce more than once, i.e., “kelts”, on r and Pr(r < 0). For the life-history scenarios examined here, dams are forecast to negatively affect persistence, even at the comparatively high per-dam smolt survival rate of 90%. As the number of dams increases from one to four, the probability of negative population growth increases four-fold from 10% to 47%. Kelt survival rate, number of dams, and smolt dam-passage survival were all found to be significant factors in predicting population persistence. The present study suggests two primary conclusions: (1) dams are likely to have a negative influence on Atlantic salmon and (2) kelts can have considerable and positive influence on population viability. Our work provides compelling support for the hypothesis that mortality attributable to dam facilities can adversely affect survival, persistence, and recovery of depleted migratory fish populations.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".