Estimating catchability and density of the European lobster Homarus gammarus from continuous, short-term mark-recapture data
Bibliographic record
Abstract
Despite the commercial and ecological importance of the European lobster Homarus gammarus, estimates of the population dynamics within socio-economically important fishing areas remain understudied. We implemented a mark-recapture approach to estimate population density, rates of loss and catchability of H. gammarus off the coast of northeast England, an important area for lobster fishers, and one of high exploitation. The short-term study used continuous trapping data from a commercial parlour trap array, fished over 6 wk. Over 9 haul occasions, 562 lobsters were marked using persistent external T-bar tags with unique ID numbers; 13.7% of these lobsters were subsequently recaptured. Catch data were used to determine the relationship between trap soak time and the effective fishing effort over time. Capture histories and effort data were analysed using a modified Cormack-Jolly-Seber (CJS) model, adapted for the short-term and continuous nature of the study. Model estimates of male lobster density varied depending on capture occasion between 732 (95% CI = 423, 1267) and 2730 (95% CI = 1585, 4701) lobsters per km2. Similarly, female density was estimated at between 2410 (95% CI = 476, 12176) and 8060 (95% CI = 1592, 40810) lobsters per km2. Low rates of loss of individuals from the area and large differences in catchability between sexes led to a female-skewed density estimate. If these findings are corroborated, the effects of sex-specific catchability and the potential for biased sex composition in populations and catches should be addressed in stock assessments and when interpreting sex ratio data in commercial catches.
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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 | 0.006 |
| 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 | 0.001 |
| 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".