Ghosts of fisheries-induced depletions: do they haunt us still?
Bibliographic record
Abstract
Abstract The depletion of several North Atlantic gadoids in the 1980s and 1990s stimulated an unprecedented amount of research on the recovery of marine fish. Some of this work addressed long-standing questions of teleost population dynamics: (i) Does per capita population growth rate (r) always increase as abundance declines? (ii) Do teleost fish possess greater intrinsic recovery abilities (as reflected by rmax) than terrestrial vertebrates? (iii) Does the magnitude of population reduction influence the probability of recovery? (iv) If fish evolve in response to fishing, changing average fitness and thus rmax, are the population-dynamic consequences likely to be negative, positive, or neutral? Challenging some long-standing perceptions and beliefs, recent meta-analyses and empirically based model simulations on marine teleost fish support theoretically based postulates that: (i) rmax does not differ from that of terrestrial mammals; (ii) high fecundity has no influence on recovery potential; (iii) Allee effects can be manifest in some depleted populations; (iv) the greater the magnitude of population reduction, the greater the uncertainty of recovery; and (v) the consequences of fisheries-induced evolution for recovery need not always be negative. An emerging imperative of the work examined here is the need to more fully embrace and comprehensively examine the links that exist between fitness and per capita population growth, given that what happens at the level of the individual will have consequences for how populations respond to natural and anthropogenic environment change.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".