Searching for threshold shifts in spawner–recruit data
Bibliographic record
Abstract
Relationships between spawning fish (S) and surviving offspring (recruits, R) are typically assumed to be continuous and nonlinear. However, R may change abruptly with small changes in S if population-, community-, or ecosystem-scale processes trigger low adult reproduction and cause populations to shift abruptly to regimes of low recruitment. We simulated R with low mean and variation below a known S threshold and high mean and variation of R above it. We compared simulations with published S–R relationships. For all data, we fit a conventional Ricker-type S–R model, a logistic depensatory model, and also searched for an S breakpoint with a nonparametric test. The Ricker and logistic models often fit discontinuous simulated data. The nonparametric test found the S threshold in simulated data, although its accuracy depended on underlying distributions. The Ricker and logistic models and the nonparametric test identified apparent relationships within published data, sharing common results in <50% of the data sets. Although population models often assume continuous relationships, discontinuous threshold changes in R with small changes in S may occur. Identification of the conditions that reproductive state changes abruptly in fish populations may be necessary to develop risk-averse regulatory policies.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".