Into the wild: microbiome transplant studies need broader ecological reality
Bibliographic record
Abstract
Gut microbial communities (microbiomes) profoundly shape the ecology and evolution of multicellular life. Interactions between host and microbiome appear to be reciprocal, and ecological theory is now being applied to better understand how hosts and their microbiome influence each other. However, some ecological processes that underlie reciprocal host-microbiome interactions may be obscured by the current convention of highly controlled transplantation experiments. Although these approaches have yielded invaluable insights, there is a need for a broader array of approaches to fully understand host-microbiome reciprocity. Using a directed review, we surveyed the breadth of ecological reality in the current literature on gut microbiome transplants with non-human recipients. For 55 studies, we categorized nine key experimental conditions that impact the ecological reality (EcoReality) of the transplant, including host taxon match and donor environment. Using these categories, we rated the EcoReality of each transplant. Encouragingly, the breadth of EcoReality has increased over time, but some components of EcoReality are still relatively unexplored, including recipient host environment and microbiome state. The conceptual framework we develop here maps the landscape of possible EcoReality to highlight where fundamental ecological processes can be considered in future transplant experiments.
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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 | 0.074 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".