Implications of extremely high recruitment: crowding and reduced growth within spatial closures
Bibliographic record
Abstract
The influence of density on population dynamics is a fundamental concept of ecology; however, observations of marine populations affected by density are rare.Recently, extremely high abundances of Atlantic sea scallops Placopecten magellanicus have persisted over a wide range of their distribution, including 2 adjacent areas on Browns Bank, Canada, that were closed to fishing from 2014 to 2016.We hypothesized that the closures, named C2 and C3, would work as expected, i.e. scallop abundance would decrease through time due to natural mortality, while yield would substantially increase due to growth.To test this, we compared the density (number of scallops per unit area), crowding levels (number of scallops around an individual scallop), and shell growth of scallops through the closures.Despite similar scallop densities in each area, crowding levels were significantly higher within area C2 than in C3.This contrasting result suggests the intensity of scallop aggregation was significantly higher in area C2.This area also had individuals with reduced shell height growth in the final year of the closure.Combined, the results show yield was lost between Years 2 and 3 in area C2 while yield increased through time in area C3.The different levels of crowding between the 2 areas may explain the different growth patterns; factors causing discrepancies in scallop growth can be related to aggregation intensity.Spatial closures to increase fishery yield are commonly thought of as 'money in the bank', but divergences from typical patterns, in this case growth, suggest considering these areas as shorter term 'windows of opportunity' will help their 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".