The influence of blue crab movement on mark–recapture estimates of recreational harvest and exploitation
Bibliographic record
Abstract
Despite the need to quantify total catch to support sustainable fisheries management, estimating harvests of recreational fishers remains a challenge. Harvest estimates from mark–recapture studies have proven valuable, yet animal movements and migrations may bias some of these estimates. To improve recreational harvest estimates, explore seasonal and spatial harvest patterns, and understand the influence of animal movement on exploitation rates, we conducted a mark–recapture experiment for the blue crab (Callinectes sapidus) fishery in Maryland waters of Chesapeake Bay, USA. Data were analyzed with standard tag-return methods and with revised equations that accounted for crab movement between reporting areas. Using standard calculations, state-wide recreational harvest was estimated to be 4.04 million crabs. When movement was included in the calculations, the estimate was 5.39 million, an increase of 34%. With crab movement, recreational harvest in Maryland was estimated to be 6.5% of commercial harvest, a finding consistent with previous effort surveys. The new methods presented herein are broadly applicable for estimating recreational harvest in fisheries that target mobile species and for which spatial variation in commercial harvest is known.
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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 | 0.007 | 0.025 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".