Production rates of walleye and their relationship to exploitation in Escanaba Lake, Wisconsin, 1965–2009
Bibliographic record
Abstract
Understanding variability in fish production, biomass, production/biomass (P/B) ratios, and their relationship to exploitation is central to fisheries sustainability. At Escanaba Lake, Wisconsin, USA, data from a compulsory creel census (1965–2009) were combined with survey data on fish populations to test for empirical relationships between annual production and exploitation rates of walleye (Sander vitreus). Empirical estimates of walleye production were relatively high and temporally variable in Escanaba Lake. Annual production, biomass, and P/B ratios ranges were 2.4–11.3 kg·ha −1 ·year −1 , 9.1–49.4 kg·ha −1 , and 0.15–0.30 year −1 , respectively. Walleye production rates were significantly and positively correlated with adult (i.e., age 3+) walleye density — the core metric used to manage walleye populations in the Ceded Territory of Wisconsin. However, adult walleye density correlated best with ages 4–6 production. Annual exploitation based on the percentage of individuals removed from the population was linearly related to annual exploitation based on the percentage of production removed. On average, 100% of annual production was harvested at ∼20% annual exploitation of individuals; thus, higher sustained exploitation rates would deplete walleye biomass in Escanaba Lake over time. A sustainable annual exploitation rate of 20% annually is lower than the 35% limit reference point currently used for walleye management in the Ceded Territory of Wisconsin. Nevertheless, our estimate is strongly concordant with two recent and independent modeling efforts addressing this same topic. We suggest empirical estimates of fish production are a vastly underutilized tool for better understanding sustainable exploitation rates in walleye populations and, likely, fisheries in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".