Habitat effects on American lobster (<i>Homarus americanus</i>) movement and density: insights from georeferenced trap arrays, seabed mapping, and tagging
Bibliographic record
Abstract
Understanding the influence of heterogeneous marine landscapes on the movements of benthic megafauna is often hampered by limited spatial resolution and insufficient sample size. Here, we combined the benefits of seabed mapping, georeferenced trap arrays, and conventional tagging methods to quantify the effect of substrate on movements of the American lobster ( Homarus americanus ). In total, 21 848 lobsters were tagged, and movements were tracked among spatially referenced research and commercial traps. We found that lobster densities from diver surveys were highest on rocky habitat, but catch rates in traps were highest on unstructured sediment, resulting in traps on level bottom having a larger effective fishing area than traps on structurally complex habitat. Moreover, tag returns indicated that lobsters initially caught and released on sediment moved farther and faster than those initially caught in traps on rocky substrate. These observations are consistent with previous reports of the existence of a dichotomy of transient and resident lobsters in coastal populations, but the association of movement with habitat type was unknown. Our results indicate that field studies integrating conventional trapping, visual census, and tagging with seabed mapping can efficiently generate high-resolution information on habitat-related behavior of large samples of benthic organisms.
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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 | 0.000 | 0.000 |
| 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".