It's what's on the inside that counts: stress physiology and the bacterial microbiome of a wild urban mammal
Bibliographic record
Abstract
The microbiome's capacity to shape the host phenotype and its mutability underlie theorization that the microbiome might facilitate host acclimation to rapid environmental change. However, when environmental change occurs, it is unclear whether resultant microbiome restructuring is proximately driven by this changing external environment or by the host's physiological response to this change. We leveraged urbanization to compare the ability of host environment (urban or forest) versus multi-scale biological measures of host hypothalamic–pituitary–adrenal (HPA) axis physiology (neutrophil : lymphocyte ratio, faecal glucocorticoid metabolites, hair cortisol) to explain variation in the eastern grey squirrel (Sciurus carolinensis) faecal microbiome. Urban and forest squirrels differed across all three of the interpretations of HPA axis activity we measured. Direct consideration of these physiological measures better explained greater phylogenetic turnover between squirrels than environment. This pattern was strongly driven by trade-offs between bacteria which specialize on metabolizing digesta versus host-derived nutrient sources. Drawing on ecological theory to explain patterns in intestinal bacterial communities, we conclude that although environmental change can affect the microbiome, it might primarily do so indirectly by altering host physiology. We demonstrate that the inclusion and careful consideration of dynamic, rather than fixed (e.g. sex), dimensions of host physiology are essential for the study of host–microbe symbioses at the micro-evolutionary scale.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".