Breeding microbes: How genetic diversity shapes gut microbial communities in the critically endangered European mink (Mustela lutreola)
Bibliographic record
Abstract
Host’s fitness can be affected by its genotype and gut microbiota, defined as the microbes living in the host’s intestinal tract. This study explored how the genetic diversity of the host influences its bacterial communities in the context of captive breeding programs, for the critically endangered European mink (Mustela lutreola). As stated by the ecosystem on a leash model, mechanisms such as inbreeding depression may lead to changes in immunomodulation and will therefore induce modifications of the gut microbiota. We investigated variation in the gut bacteria through 16S rRNA metabarcoding, related to the genetic diversity of European mink held in captivity in two breeding centers representing separate breeding stocks originating from the western and eastern populations. The genetic diversity of the host was assessed through diversity analysis of the adaptive MHC class I and II genes as well as neutral microsatellite markers. Results indicate lower diversity in neutral and MHC class I genes for the western population, and the opposite for MHC class II. A lower MHC class II gene variability led to an increase in microbial phylogenetic diversity and in abundance depending on the presence of specific MHC-II motifs. This shows the importance of integrating both neutral and adaptive markers when investigating genetic variation in the context of ex situ conservation, as well as gut microbial community assessment. We advocate for more natural mating systems in captive breeding program to foster genetic diversity as a whole to mitigate the effects of genetic drift on those small, isolated populations.
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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 | 0.000 |
| 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".