Empirical links between natural mortality and recovery in marine fishes
Bibliographic record
Abstract
Probability of species recovery is thought to be correlated with specific aspects of organismal life history, such as age at maturity and longevity, and how these affect rates of natural mortality (M) and maximumper capitapopulation growth (rmax). Despite strong theoretical underpinnings, these correlates have been based on predicted rather than realized population trajectories following threat mitigation. Here, we examine the level of empirical support for postulated links between a suite of life-history traits (related to maturity, age, size and growth) and recovery in marine fishes. Following threat mitigation (medium time since cessation of overfishing = 20 years), 71% of 55 temperate populations had fully recovered, the remainder exhibiting, on average, negligible change (impaired recovery). Singly, life-history traits did not influence recovery status. In combination, however, those that jointly reflect length-based mortality at maturity,Mα, revealed that recovered populations have higherMα, which we hypothesize to reflect local adaptations associated with greaterrmax. But, within populations, the smaller sizes at maturity generated by overfishing are predicted to increaseMα, slowing recovery and increasing its uncertainty. We conclude that recovery potential is greater for populations adapted to highMbut that temporal increases inMconcomitant with smaller size at maturity will have the opposite effect. The recovery metric documented here (Mα) has a sound theoretical basis, is significantly correlated with direct estimates ofMthat directly reflectrmax, is not reliant on data-intensive time series, can be readily estimated, and offers an empirically defensible correlate of recovery, given its clear links to the positive and impaired responses to threat mitigation that have been observed in fish populations over the past three decades.
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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 | 0.002 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".