Modeling population dynamics and nonstationary processes of difficult-to-age fishery species with a hierarchical Bayesian two-stage model
Bibliographic record
Abstract
Modeling population dynamics and establishing a comprehensive population assessment for fishery species that are difficult to age have been challenging. Determination of age for such species is still an unresolved issue or is at best uncertain. Catch-survey analysis does not require full age information but can still provide a comprehensive population assessment. It was extended to incorporate multiple surveys and multiple sources of uncertainties within the statistical catch-at-age framework in the applications to crustaceans. Here, we further generalize and extend the multiple survey catch-survey analysis into a hierarchical Bayesian two-stage model by applying the hierarchical Bayesian approach. The hierarchical Bayesian approach can sufficiently incorporate uncertainty and expert opinions in parameter estimation. We developed a series of models with different assumptions for natural mortality and catchability, including nonstationary (i.e., time-varying) assumptions. We evaluated model robustness to these assumptions and compared population dynamics estimates and population status determination. We demonstrated the application of the hierarchical Bayesian two-stage model using the North Carolina blue crab (Callinectes sapidus) example. In this example, estimation of population size and fishing mortality and determination of population status were robust to the natural mortality and catchability assumptions. The North Carolina blue crab population is less likely to have nonstationary catchability or nonstationary natural mortality. Its natural mortality is more likely to vary by stage than by sex or over time.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".