Recruitment and survival rate variability in fish populations: density-dependent regulation or further evidence of environmental determinants?
Bibliographic record
Abstract
Recently, Minto et al. (2008) , based on a fishery data set including marine, estuarine, and freshwater fishes, described higher variability in the survival rates of juveniles at low rather than at high parental density in an inversely density-dependent fashion and suggested density-dependent mechanisms underpinning those patterns. This study, based on a long-term study of brown trout (Salmo trutta; a species and habitat not included in the Minto et al. (2008) analysis), documents that survival rates in these stream-living populations exhibit a pattern that matches exactly those reported by Minto et al. (2008) . Nevertheless, hypothesis testing rejected the occurrence of stock–recruitment relationships and the operation of density-dependent recruitment regulation. The patterns elucidated for these brown trout populations can be entirely explained by the operation of two single environmental factors, namely, stream discharge in March determining annual survival rates across streams and sites and site-specific depth determining site-specific survival rates. It is open to question that exactly the same patterns can be generated by two sets of opposing factors, density-dependent (i.e., Minto et al. 2008 ) and environmental factors (i.e., this study). The consistency of this pattern suggests that survival rates and recruitment are probably determined by environmental factors across fish populations and habitats.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".