Attraction and repulsion of mobile wild organisms to finfish and shellfish aquaculture: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Knowledge of aquaculture–environment interactions is essential for the development of a sustainable aquaculture industry and efficient marine spatial planning. The effects of fish and shellfish farming on sessile wild populations, particularly infauna, have been studied intensively. Mobile fauna, including crustaceans, fish, birds and marine mammals, also interact with aquaculture operations, but the interactions are more complex and these animals may be attracted to (attraction) or show an aversion to (repulsion) farm operations with various degrees of effects. This review outlines the main mechanisms and effects of attraction and repulsion of wild animals to/from marine finfish cage and bivalve aquaculture, with a focus on effects on fisheries‐related species. Effects considered in this review include those related to the provision of physical structure (farm infrastructure acting as fish aggregating devices ( FAD s) or artificial reefs ( AR s), the provision of food (e.g. farmed animals, waste feed and faeces, fouling organisms associated with farm structures) and some farm activities (e.g. boating, cleaning). The reviews show that the distribution of mobile organisms associated with farming structures varies over various spatial (vertical and horizontal) and temporal scales (season, feeding time, day/night period). Attraction/repulsion mechanisms have a variety of direct and indirect effects on wild organisms at the level of individuals and populations and may have implication for the management of fisheries species and the ecosystem in the context of marine spatial planning. This review revealed considerable uncertainties regarding the long‐term and ecosystem‐wide consequences of these interactions. The use of modelling may help better understand consequences, but long‐term studies are necessary to better elucidate effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it