Temporary Allee effects among non‐stationary recruitment dynamics in depleted gadid and flatfish populations
Bibliographic record
Abstract
Abstract Many considerably declined fish populations have not fully recovered despite reductions in fishing pressure. One of the possible causes of impaired recovery is the (demographic) Allee effect. To investigate whether low‐abundance recruitment dynamics can switch between compensation and depensation, the latter implying the presence of the Allee effect, we analysed the stock–recruitment time series of 17 depleted cod‐type and flatfish populations using a Bayesian change point model. The recruitment dynamics were represented with the sigmoidal Beverton–Holt and the Saila–Lorda stock–recruitment models, allowing the parameters of the models to shift at a priori unknown change points. Our synthesis study questions the common assumption that recruitment is stationary and compensatory and the high amount of scatteredness often present in stock–recruitment data is only due to random variation. When a moderate amount of such variation was assumed, stock–recruitment dynamics were best explained by a non‐stationary model for 53% of the populations, which suggests that these populations exhibit temporal changes in the stock–recruitment relationship. For four populations, we found shifts between compensation and depensation, suggesting the presence of temporary Allee effects. However, the evidence of Allee effects was highly dependent on the priors of the stock–recruitment model parameters and the amount of random variation assumed. Nonetheless, detection of changes in low‐abundance recruitment is essential in stock assessment since such changes affect the renewal ability of the population and, ultimately, its sustainable harvest limits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".