Seasonal dynamics in the mammalian microbiome between disparate environments
Bibliographic record
Abstract
Host-associated bacterial microbiomes can facilitate host acclimation to seasonal environmental change and are hypothesized to help hosts cope with recent anthropogenic environmental perturbations (e.g., landscape modification). However, it is unclear how recurrent and recent forms of environmental change interact to shape variation in the microbiome. The majority of wildlife microbiome research occurs within a single seasonal context. Meanwhile, the few studies of seasonal variation in the microbiome often restrict focus to a single environmental context. By sampling urban and exurban eastern grey squirrel populations in the spring, summer, autumn, and winter, we explored whether seasonal rhythms in the grey squirrel gut microbiome differed across environments using a 16S amplicon sequencing approach. Differences in the microbiome between urban and exurban squirrels persisted across most of the year, which we hypothesize is linked to anthropogenic food consumption; but we also observed similarities in the urban and exurban grey squirrel microbiome during the autumn, which we attribute to engrained seed caching instincts in preparation for the winter. Host behaviour and diet selection may therefore be capable of maintaining similarities in microbiome structure between disparate environments. However, the depletion of an obligate host mucin glycan specialist (Akkermansia) during the winter in both urban and exurban squirrels was among the strongest differential abundance patterns we observed. In summary, urban grey squirrels showed different seasonal patterns in their microbiome than squirrels from exurban forests, however, in some instances host behaviour and physiological responses might be capable of maintaining similar microbiome responses across seasons.
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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 | 0.001 |
| 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 | 0.001 |
| 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".