Spatial modeling of Bering Sea walleye pollock with integrated age-structured assessment models in a changing environment
Bibliographic record
Abstract
Climate change may affect the spatial distribution of fish populations in ways that would affect the accuracy of spatially aggregated age-structured assessment models. To evaluate such scenarios, spatially aggregated models were compared with spatially explicit models using simulations. These scenarios were based on hypothetical climate-driven distribution shifts and reductions in mean recruitment of walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea. Results indicate that biomass estimates were reasonably accurate for both types of estimation models and precision improved with the inclusion of tagging data. Bias in some aggregated model scenarios could be attributed to unaccounted-for process errors in annual fishing mortality rates and was reduced when estimating effective sample size or time-varying selectivity. Spatially explicit models that allow estimation of variability in movement and ontogenetic parameters (specified as a random walk process) were shown to be feasible, whereas models that misspecified ontogenetic movement and climate change effects resulted in biased biomass and movement parameter estimates. These results illustrate that more complex models may characterize processes better but may be less robust for management advice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".