Spatially Varying Population Demographics and Fishery Characteristics of Lake Erie Walleyes Inferred from a Long‐Term Tag Recovery Study
Bibliographic record
Abstract
Abstract Although the Lake Erie population of Walleyes Sander vitreus exhibits complex spatial structuring, the extent to which population demographics also vary spatially is unknown. Using a spatial tag recovery model, we estimated region‐ and age‐specific mortalities and regional movement probabilities by using recoveries from a jaw tagging study initiated in 1990. The best‐performing model based on a comparison of quasi‐likelihood Akaike's information criterion values had age‐group‐specific movement probabilities, age‐ and region‐specific natural mortalities, and age‐group‐ and region‐specific annual fishing mortalities. Commercial fishing mortality varied considerably during the study, while recreational fishing mortality was more static. Natural mortality of age‐5 and older Walleyes was lower than that of younger fish in all regions, with values ranging from 0.30 to 0.40 for age‐4 and younger fish and from 0.13 to 0.27 for age‐5 and older fish. In Lake Erie's western basin, natural mortality of age‐4 fish was lower than that of age‐3 fish. Sensitivity analyses indicated that some natural mortality estimates were sensitive to (1) prior probability distributions assigned to mortality components and (2) assumed movement probabilities in regions where no tagging was conducted. The decline in natural mortality with age in Lake Erie's western basin matches what has been found for other populations, suggesting that such patterns are perhaps common in exploited Walleye populations. Movement probabilities in the western basin were greater than those in the combined central and eastern basins. The mortality rates and movement probabilities estimated in this study should assist in the parameterization and scaling of a spatially explicit Lake Erie Walleye assessment model, the development of which has been recommended for the lake's quota management system. Our study is one of the first to apply a spatial tag recovery model to a freshwater fish population for estimating mortality components. We encourage wider use of this method to improve the understanding of how mortality components and movements vary regionally within freshwater systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".