Population dynamics of marine fishes at low abundance
Bibliographic record
Abstract
The recovery of depleted species depends on their population dynamics at low abundance. Classical population growth models, applied widely in fisheries science, assume that per capita offspring production increases as abundance declines (compensation). However, slow or absent recovery by many depleted fishes might reflect unexpectedly weak compensation or the presence of Allee effects (depensation). Using meta-analytical techniques to describe reproductive dynamics, we find considerable variability among 207 exploited marine fish stocks (104 species) in how standardized per capita population growth changes with abundance. Although many species exhibit strong compensatory dynamics (negative density dependence), others show much weaker compensation than expected, and some exhibit evidence of an Allee effect, such as Atlantic cod ( Gadus morhua ) and Alaskan walleye pollock ( Theragra chalcogramma ). As data at low levels of abundance become increasingly available, it appears that compensation, while strong in some species, is comparatively weak or nonexistent in others, thus providing an explanation for why the recovery of some depleted stocks, despite reductions in exploitation, has been considerably less than what classic models of population growth would otherwise suggest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".