Is lack of space a limiting factor for the development of aquaculture in EU coastal areas?
Bibliographic record
Abstract
This study examines the spatial occupancy of marine finfish aquaculture in the European Union (EU), identifies geographical clusters and administrative areas where cage aquaculture development is particularly significant and provides evidence on the interactions between aquaculture and the touristic use of the coastline. Despite the increasing demand for seafood in the EU, its aquaculture is not expanding at the same rate ( FAO, 2014 ), and the low number of new licences issued in recent years is a clear sign of the difficulties of the sector to expand. In this study, Google Earth satellite images and GIS methods were used to map and analyse spatial properties of marine finfish aquaculture sites in the EU. The analysis covers ten member states (Cyprus, Spain, France, Greece, Croatia, Ireland, Italy, Malta, Slovenia, United Kingdom) representing around 95% of EU marine finfish aquaculture production by volume, and Turkey. The results indicate that existing marine aquaculture sites occupy around 230 hectares (ha) in Greece, and 34 ha in UK, which represent respectively 28% and 44% of EU marine finfish production by volume. Considering these very low figures of occupied surface, it is difficult to imagine that the expansion of marine aquaculture in the EU would be constrained by a lack of space in absolute terms. Limitations to growth may be better explained by the competition for space which takes place at the local level with more established coastal economic activities. To examine in particular the interactions with the touristic use of the coastline, the analysis considered the distribution of hotels around the aquaculture sites and found that there is evidence of strong negative spatial interaction up to a distance of 3 km. These quantitative findings corroborate more qualitative considerations on the conflicts affecting the establishment of marine aquaculture in specific coastal regions in USA , Canada, Australia and New Zealand described in the literature. Another contribution from this study lies in the identification and mapping of geographical clusters and local administrative units where aquaculture production is particularly significant. Since socio-economic data for the individual aquaculture sites in the EU are not easily accessible, the mapping of EU aquaculture clusters is the prerequisite for further research to understand the local enabling conditions apart from bio-physical conditions which favoured the expansion of aquaculture in specific areas and not in others and identifying examples of best practices for the governance of the sector.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".