Global estimation of areas with suitable environmental conditions for mariculture species
Bibliographic record
Abstract
Aquaculture has grown rapidly over the last three decades expanding at an average annual growth rate of 5.8% (2005-2014), down from 8.8% achieved between 1980 and 2010. The sector now produces 44% of total food fish production. Increasing demand and consumption from a growing global population are driving further expansion of both inland and marine aquaculture (i.e., mariculture, including marine species farmed on land). However, the growth of mariculture is dependent on the availability of suitable farming areas for new facilities, particularly for open farming practices that rely on the natural oceanic environmental parameters such as temperature, oxygen, chlorophyll etc. In this study, we estimated the marine areas within the exclusive economic zones of all countries that were suitable for potential open ocean mariculture activities. To this end, we quantify the environmental niche and inferred the global habitat suitability index (HSI) of the 102 most farmed marine species using four species distribution models. The average weighted HSI across the four models suggests that 72,000,000 km2 of ocean are to be environmentally suitable to farm one or more species. About 92% of the predicted area (66,000,000 km2) is environmentally suitable for farming finfish, 43% (31,000,000 km2) for molluscs and 54% (39,000,000 km2) for crustaceans. These predictions do not consider technological feasibility that can limit crustaceans farming in open waters. Suitable mariculture areas along the Atlantic coast of South America and West Africa appear to be most under-utilized for farming. Our results suggest that factors other than environmental considerations such as the lack of socio-economic and technological capacity, as well as aqua feed supply are currently limiting the potential for mariculture expansion in many areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".