Implementing a fishery improvement programme to manage the common cuttlefish (Sepia officinalis) in artisanal sea and lagoon fisheries: The case study of the Chioggia’s fleet
Bibliographic record
Abstract
In the light of reducing the impact of fishery, artisanal fisheries hold great potential to achieve the sustainable use of marine resources if locally managed at sub-regional level (i.e., within the same province or fleet) and by implementing mitigation systems. The Chioggia small-scale fishing fleet represents a study case in an overexploited Mediterranean sub-region, operating both in the Venice Lagoon and the North Adriatic Sea. During the spawning period, this small-scale fishery targets the same target species, the common cuttlefish ( Sepia officinalis ), which, over the past thirty years, has shown a general decline in catch trends. Cuttlefish represent a high-value resource at all life stages, with both adults and juveniles considered targets of the artisanal fishery. Additionally, eggs are consistently laid on fishing gear during the breeding season and must be removed by fishers to prevent a reduction in fishing efficiency. These aspects contribute to the vulnerability of the common cuttlefish stocks, calling for a complementary management approach. We researched methods from the peer-reviewed literature, and adapted methods to reflect the environmental conditions of the sea and lagoon, and small-scale fishing methods. We combined into a complementary management approach: i) mitigation systems to reduce and collect eggs discarded during fishing operations, ii) semi-natural juvenile rearing diets to potentially release fishing pressure and support natural population, and iii) small-scale supply chain to both raise awareness about this resource vulnerability and promote the activity of fishers adopting virtuous behaviours. We tested these three functional management units in collaboration with lagoon and sea fishers and local stakeholders. We showed that the use of egg collectors, as an alternative spawning structure, performed well to limit the loss of the eggs laid upon the fishing gears. Second, we showed that Mixed and Natural diets contributed significantly to the growth rate of hatchlings compared to artificial diets. Finally, we demonstrated a shared interest in products derived from sustainable fisheries from the public, fishers and retailers. Therefore, the small-scale supply chain can represent a valid component to valorise the adoption of the whole management scheme. Overall, this complementary management approach can be adapted to and implemented in other local socio-economic communities of artisanal fishing in coastal environments worldwide. Complementary mitigation systems could work in synergy, overcoming the limitations of individual mitigation measures applied to diverse fishery resources.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".