Life history correlates of density-dependent recruitment in marine fishes
Bibliographic record
Abstract
Understanding the relationships among life history traits, density dependence, and population dynamics is a central goal in ecology. It is also vital if we are to predict how populations respond to and recover from exploitation. We used data for 54 stocks of commercially exploited fish species to examine relationships between maximum annual recruitment at low stock size and the density dependence of recruitment at high stock size. We then related these recruitment measures to life history. At low stock sizes, stocks with high maximum recruitment (maximum spawners per spawner) showed the weakest density dependence of recruitment at high stock sizes. Spawning biomass per recruit in the absence of fishing (SPRF=0) showed a strong positive correlation with both maximum spawners per spawner and the strength of density dependence. Stocks with high SPRF=0 were typically large-bodied, slow-growing, late-maturing, and highly fecund with long generation times. These stocks produced low numbers of recruits each year, but survived to breed repeatedly and had strong density dependence of recruitment. In contrast, small-bodied, early-maturing fish had high annual recruitment and weak density dependence. These results place species on a continuum from "highly reproductive" to "survivors". But we also demonstrate that density dependence is an important feature of the population biology of survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".