Dynamic models for fish stock productivity: state-space hidden Markov models and mixture models
Bibliographic record
Abstract
Hidden Markov models (HMMs) and state-space models (SSMs) are complementary methodologies for capturing discrete (regime-like) and continuous variations, respectively, in the sense that HMMs typically use separate parameter sets for each regime, whereas SSM parameters evolve continuously and are often correlated over time. In this work, we combine the strengths of both approaches by developing HMMs with serial correlation and implementing them efficiently. Resolving recruitment productivity changes is crucial to effective fisheries management as shifts in the stock-recruitment (SR) relationship redefines levels of sustainable removals. To account for interactions between SR parameters and other components of stock assessment models, we embed hidden Markov SR models within the broader stock assessment framework. Additionally, we incorporate covariates into the transition probabilities of the HMM to address nonstationarity and substantially reduce model complexity. Simulation and case studies demonstrate the strong performance of this novel methodology. This study also investigates temporal changes in the maturation dynamics of American plaice using three modeling approaches: an SSM, an HMM, and an additive logistic mixture model (ALMM). Fisheries management is usually focused on maintaining the mature component of a stock at a level expected to maximize egg production and future stock productivity. The mature stock is typically measured using the spawning stock size, which depends on the proportion mature-at-age or length (i.e., maturity). Many stocks in the Newfoundland and Labrador region have experienced large changes in maturity over time. The SSM captures strong temporal dependence and gradual shifts in the age at 50% maturity. The HMM identifies eight discrete regimes representing abrupt changes in maturation parameters, while the ALMM, with five regimes, provides the best fit, capturing distinct maturation patterns across regimes. Collectively, these models reveal both continuous and regime-like changes in maturation, offering valuable insights into life-history variability and its implications for population dynamics.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".