Estimating walleye (<i>Sander vitreus</i>) movement and fishing mortality using state-space models: implications for management of spatially structured populations
Bibliographic record
Abstract
Fish often exhibit complex movement patterns, and quantification of these patterns is critical for understanding many facets of fisheries ecology and management. In this study, we estimated movement and fishing mortality rates for exploited walleye (Sander vitreus) populations in a lake-chain system in northern Michigan. We developed a state-space model to estimate lake-specific movement and fishery parameters and fit models to observed angler tag return data using Bayesian estimation and inference procedures. Informative prior distributions for lake-specific spawning-site fidelity, fishing mortality, and system-wide tag reporting rates were developed using auxiliary data to aid model-fitting. Our results indicated that postspawn movement among lakes was asymmetrical and ranged from approximately 1% to 42% per year, with the largest outmigration occurring from the Black River, which was primarily used by adult fish during the spawning season. Instantaneous fishing mortality rates differed among lakes and ranged from 0.16 to 0.27, with the highest rate coming from one of the smaller and uppermost lakes in the system. The approach developed provides a flexible framework that incorporates seasonal behavioral ecology (i.e., spawning-site fidelity) in estimation of movement for a mobile fish species that will ultimately provide information to aid research and management for spatially structured fish populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".