Does recruitment trigger negative density-dependent feedback loops in stream-dwelling salmonids?
Bibliographic record
Abstract
I explored the hypothesis that recruitment may stabilize the numerical dynamics of stream-dwelling salmonids by triggering density-dependent feedback loops through the operation of recruitment dependence on individual growth, mortality, life span and maximum size and their effects on fecundity. I examined 98 cohorts of two Salmo trutta populations of northern Spain and a population of Jutland (Denmark) located 2400 km apart. Recruitment, growth, mortality, life span and maximum size were inter-related, were recruitment-dependent and described negative power trajectories. In the Spanish populations, faster-growing individuals of weakly recruited cohorts with lower mortality attain longer life span and larger size. Hence, larger females spawning more abundant, larger eggs that, in turn, induce stronger cohorts of higher spawners’ abundance, recruitment and mortality. The mortality patterns match the self-thinning patterns, an ultimate expression of competition. Significant relationships among self-thinning slopes and mortalities rates with increasing recruitment demonstrate that the rate at which density-driven mortality is higher, the stronger the intensity of intraspecific competition. Space-limited habitat and size-dependent resource availability underpinning site-specific carrying capacities suggest that interference competition is the primary mechanism underpinning population regulation.
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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 | 0.002 |
| 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".