Potential nutrient, carbon and fisheries impacts of large-scale seaweed and shellfish aquaculture in Europe evaluated using operational oceanographic model outputs
Bibliographic record
Abstract
Large-scale seaweed and shellfish aquaculture are increasingly being considered by policymakers as a source of food, animal feed and bioproducts for Europe. These aquacultures are generally thought to be low impact or even beneficial for marine ecosystems as they are ‘extractive’ – i.e., growing passively on foodstuff already available in seawater, and with potential habitat provision, fisheries, climate mitigation and eutrophication mitigation benefits. At some scale however, over-extraction of nutrients or chlorophyll could potentially have a negative effect on natural systems. Understanding the likely impacts of aquaculture production at scale is important to identify when safe limits are being approached. Taking seaweed aquaculture as the primary focus, this work uses operational oceanographic model outputs to drive prognostic growth models to predict the likely optimal distribution of seaweed farms across European waters to meet different production scenarios. A novel nutrient transport scheme is then used to model the interacting ‘footprints’ of nutrient drawdown from aquaculture facilities to demonstrate the likely spatial impact of large-scale aquaculture. Evaluation of both seaweed and shellfish contributions to CO2 balance under large scale production, and the potential impact on fisheries are also considered. The study finds that the impact of intensive seaweed aquaculture on nutrient availability could be significant where many farms are placed close together; but at the regional/basin scale even the highest level of production considered does not significantly impact total nutrient budgets. Seaweed aquaculture has the potential to extract large amounts of carbon dioxide, but the impact on carbon budgets depends on the end-use of the extracted seaweed. Shellfish aquaculture is a net source of CO2 due to the impact of calcification of shells on the carbonate system (i.e., alkalinity removal). However, gram-for-gram the CO2 impact of shellfish production is likely to be less than the impact of land-based meat production. Whilst operational oceanographic models are useful for taking a ‘broad brush’ approach to likely placement and impacts of aquaculture, reliable yield predictions for individual locations across European waters would require models integrating more physical and biogeochemical factors (wave environment, local currents, riverine inputs) at a finer scale than currently achievable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".