Simulation‐based investigations of fishery changes as affected by the scale and design of artificial habitats
Bibliographic record
Abstract
Preliminary field observations on a large‐scale multi‐reef artificial reef system in Scotland indicated that the provision of artificial habitat of varying design alters the numbers and types of fishes present in areas where they were previously largely absent. These modifications could, in time, be highly beneficial to any programme of fishery enhancement, protection or restoration but only where the scale of habitat manipulation was relevant to the target fisheries. Ecosystem simulations provide a theoretical basis on which to conduct examinations of habitat manipulation at scales that could not be tested empirically. In the present study, a series of ecosystem models was constructed based on broad‐scale simulations for the west coast of Scotland in order to examine the potential consequences to selected fisheries of different scales and types of intervention using artificial reefs. Initialized with a large‐scale mass‐balance model, a number of smaller‐scale dynamic ecosystem simulations covered management situations that encompassed habitat type and reef design for open, restricted and closed fisheries. Spatial simulations examined the presence and absence of artificial habitat with natural reefs and marine protected areas (MPAs) under realistic environmental situations. The simulated trends supported preliminary field observations that artificial habitats would support similar biotic aggregations to natural reefs. Designs that maximized the reef edge as a function of the total reef volume were more productive for some of the functional groups examined compared with larger single deployments of identical area. Increasing the area of artificial habitat did result in improvements to some of the fisheries. The behaviour of some functional groups, however, highlighted the limitations of the broad‐scale primary model to smaller‐scale investigation. Future simulations used to inform management decision at the sub‐regional level would require evidence‐based revision to improve their relevance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".