A fisheries perspective of behavioural variability: differences in movement behaviour and extraction rate of an exploited sparid, snapper (<i>Pagrus auratus</i>)
Bibliographic record
Abstract
Intraspecific variation in movement patterns are well established for many species, but poorly appreciated in fisheries management. In this study we dart-tagged snapper ( Pagrus auratus ), an important fishery species, across different areas and habitats in the Hauraki Gulf, New Zealand. Tag returns were used to quantify movement behaviour and extraction rates using a maximum likelihood model that corrected for spatial variability in population size and fishing effort. Residency was high (~90%) in two strata and lower (75%) in the remaining stratum. The stratum with the highest residency also appeared to experience the highest extraction rate (likely due to a lower population size). These results confirm the existence of differences in movement behaviour within the snapper population, suggesting that localized areas may become depleted regardless of the status of the overall stock. This has consequences for the scale of fisheries management and the size of marine reserves implemented in different regions. Understanding why variation in movement behaviour exists (i.e., genetic vs. environmental) is the next step in addressing the influence of animal behaviour on fisheries management.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".