Towards Sustainable Aquaculture: A Review on The Use of Microalgae as Functional Feed Ingredients
Bibliographic record
Abstract
Population growth is intensifying the demand for sustainable protein sources, positioning aquaculture as a strategic sector for global food security. However, the industry faces nutritional, economic, and environmental challenges, particularly due to the high cost and ecological impact of fishmeal (FM) and fish oil (FO), which are widely used in commercial feeds. These inputs are rich in essential fatty acids, such as docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), whose production relies on intensive harvesting of marine species, thereby compromising the sustainability of the supply chain. In this context, microalgae have emerged as promising alternatives due to their high nutritional and functional value, including proteins, long-chain polyunsaturated fatty acids (LC-PUFAs), antioxidants, and bioactive compounds. This review compiles scientific evidence demonstrating that the inclusion of microalgae in fish and shrimp diets can maintain or enhance lipid composition, immunocompetence, pathogen resistance, antioxidant activity, and gut health in cultured organisms. Species such as Schizochytrium sp., Nannochloropsis sp., Chlorella sp., and Spirulina sp. have shown promising results. Although further studies are needed to determine optimal inclusion levels and potential synergies among species, current data support the potential of microalgae to contribute to a more efficient and sustainable aquaculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".