Vertical Habitat Use by Adult Walleyes Conflicts with Expectations from Fishery‐Independent Surveys
Bibliographic record
Abstract
Abstract Stock assessments of Walleyes Sander vitreus in Lake Erie rely on a combination of suspended and bottom overnight gill‐net surveys to provide population and demographic information. However, the assumption that Walleyes undertake diel vertical migrations and become available to the suspended gill nets at night has never been validated. To understand how vertical habitat use affects the availability of Walleyes to fishery‐independent surveys, we compared individual behaviors observed by means of acoustic depth telemetry tags (20 individuals; 2013–2016) with catches in paired suspended and bottom gill‐net sets (273 paired sets; 2013–2016) used by management agencies. In contrast to our expectations and observations in other lakes, the mean depths for Walleyes most often occurred in the lower one‐half to one‐third of the water column, and at lake depths <25 m the fish tended to be close to the bottom. The relationship between fish and lake depth was dependent on year and season. At lake depths >15 m, Walleyes were found at shallower depths during stratified periods (i.e., summer) than during unstratified periods (autumn). They were also found at shallower depths in 2015 and 2016 than in 2013 and 2014. In paired autumn gill‐net surveys, (1) the overall proportion of Walleye catch was nearly equal in suspended and bottom gill nets and (2) the proportion of the catch in suspended gill nets declined with fish length. The pattern of decline was dependent on how deep the suspended net was fished and the year in which the data were collected. These results provide evidence that the suspended gill‐net surveys currently being used to assess Walleye demographics in Lake Erie are biased toward the capture small Walleyes, while bottom nets are biased toward the capture of large ones. Future telemetry investigations will be needed to understand whether these biases reflect differences in the depth of habitat between small and large Walleyes.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".