Artificial Rearing of Atlantic Salmon Juveniles for Supportive Breeding Programs Induces Long-Term Effects on Gut Microbiota after Stocking
Bibliographic record
Abstract
In supportive breeding programs for wild salmon populations, stocked parr experience higher mortality rates than wild ones. Among other aspects of phenotype, the gut microbiota of artificially raised parr differs from that of wild parr before stocking. Early steps of microbiota ontogeny are tightly dependent upon environmental conditions, both of which exert long-term effects on host physiology. Therefore, our objective was to assess to what extent the resilience capacity of the microbiota of stocked salmon may prevent taxonomic convergence with that of their wild congeners after two months in the same natural environment. Using the 16S SSU rRNA marker gene, we tested the general hypothesis that environmental conditions during the very first steps of microbiota ontogeny imprint a permanent effect on later stages of microbiota recruitment. Our results first showed that gut microbiota composition of stocked and wild parr from the same genetic population, and sharing the same environment, was dependent on the early rearing environment. In contrast, skin microbiota in stocked individuals converged to that of wild individuals. Taxonomic composition and co-occurrence network analyses suggest an impairment of wild bacteria recruitment and a higher instability for the gut microbiota of stocked parr. This study is the first to demonstrate the long-term effect of early microbiota ontogeny in artificial rearing for natural population conservation programs, raising the need to implement microbial ecology.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".