Spawning site fidelity and apparent annual survival of walleye (<i>Sander vitreus</i>) differ between a Lake Huron and Lake Erie tributary
Bibliographic record
Abstract
Abstract Fidelity to spawning habitats can maximise reproductive success of fish by synchronising movements to sites of previous recruitment. To determine the role of reproductive fidelity in structuring walleye Sander vitreus populations in the Laurentian Great Lakes, we used acoustic telemetry combined with Cormack–Jolly–Seber capture–recapture models to estimate spawning site fidelity and apparent annual survival for the Tittabawassee River in Lake Huron and Maumee River in Lake Erie. Walleye in spawning condition were tagged from the Tittabawassee River in Lake Huron and Maumee River in Lake Erie in 2011–2012. Site fidelity and apparent annual survival were estimated from return of individuals to the stream where tagged. Site fidelity estimates were higher in the Tittabawassee River (95%) than the Maumee River (70%) and were not related to sex or fish length at tagging. Apparent annual survival of walleye tagged in the Tittabawassee did not differ among spawning seasons but was higher for female than male walleye and decreased linearly as fish length increased. Apparent annual survival of walleye tagged in the Maumee River did not differ among spawning seasons but was higher for female walleye than male walleye and increased linearly as fish length increased. Greater fidelity of walleye tagged in the Tittabawassee River than walleye tagged in the Maumee River may be related to the close proximity to the Maumee River of other spawning aggregations and multiple spawning sites in Lake Erie. As spawning site fidelity increases, management actions to conserve population structure require an increasing focus on individual stocks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".