Cultivation of Brook Charr <i>Salvelinus fontinalis</i>: The Challenges of Disease Control and the Promise of Microbial Ecology Management
Bibliographic record
Abstract
Among salmonids, brook charr, Salvelinus fontinalis has been gaining popularity in aquaculture. However, major limitations associated with opportunistic diseases that result from unstable egg and fry‐rearing conditions are experienced under intensive rearing conditions. Great losses in brook charr culture result from viral (e.g., infectious pancreatic necrosis, viral hemorrhagic septicemia virus), fungal (e.g., saprolegniosis), and bacterial (e.g., furunculosis, cold water disease, columnaris) diseases. Unfortunately, there is no effective treatment for viral diseases for brook charr, and the chemical disinfectants and antibiotics commonly used against bacterial and fungal infections are reaching their limits (pathogen resistance, microbial community disruption), making alternative and more sustainable rearing approaches essential to bolster aquaculture. In this regard, recent studies show that many microbial species (bacteria, archaea, viruses, eukaryotes‐like protozoa, and unicellular fungi) are essential from the earliest life stages for the proper development and growth of fish. Based on ecological theory and work on marine fish, it has been hypothesized that current aquaculture practices favor r‐strategic, opportunistic microbes that are detrimental to fish health and development. Consequently, to ensure the success and sustainability of brook charr farming, a new aquaculture paradigm involving recirculating aquaculture systems (RAS) that select a K‐enriched, specialized microbial community as a preventive measure against those microbial diseases is a very promising avenue. In this review, after introducing brook charr biology, the role of microbes in brook charr rearing and the general problems of disease and low fry survival due to an imbalance in the microbial community will be discussed. Various curative and prophylactic treatment methods currently used to control these diseases and their impact on microbial ecology will be covered, and we will conclude with the rationale of microbial ecology management and its effectiveness in promoting fry survival through disease prevention. To our knowledge, this is the first literature review addressing the promises of managing microbial ecology in a sustainable way in the brook char industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".