Allee effects and compensatory population dynamics within a stock complex
Bibliographic record
Abstract
Sparked by the observation that (i) many collapsed stocks have failed to recover despite the apparent prevalence of compensatory population dynamics and (ii) many stocks have a complex structure, we devised a simulation model illustrating that the underlying behavior of substocks is masked when the data are aggregated and evaluated at the scale of the management unit. The model consisted of several substocks within a stock complex, each having its own stock and recruitment (S-R) relationship with Allee effects. Incorporation of Allee effects into the S-R relationship was achieved by specifying a critical spawning stock biomass threshold below which no recruitment occurred. The simulation model revealed that it was possible for the aggregate S-R relationship to appear compensatory, even though no substock exhibited this behavior. If the conclusions we draw from our modeling are correct, biological reference points developed from aggregated data from multiple substocks and used in conventional fisheries management and stock assessment models are likely to be inaccurate and possibly nonconservative. More research in support of the delineation of substock structure, biophysical modeling, and metapopulation theory is advocated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".