Population Dynamics and the Microbiome in a Wild Boreal Mammal: The Snowshoe Hare Cycle and Impacts of Diet, Season and Predation Risk
Bibliographic record
Abstract
The North American boreal forest is a massive ecosystem, and its keystone herbivore is the snowshoe hare (Lepus americanus). Hares are exposed to considerable environmental extremes in diet and weather, food availability, and predation risk. Gut microbiomes have been suggested to facilitate adaptive animal responses to environmental change, but severe environmental challenges to homeostasis can also disrupt host-microbiome relationships. To better understand gut microbiome contributions to animal acclimation, we studied the faecal bacterial microbiome of wild hares across two types of extreme environmental change that are integral to their natural history: (1) seasonal transitions between summer and winter, and (2) changes over the ~10 year 'boom-bust' population cycles that are characterised by shifting food resource availability and predation pressure. When compared to summer, hares in winter had lower bacterial richness and were depleted in 20 families (including Oxalobacteraceae and Christensenellaceae) but enriched for Ruminococcaceae (a family which contains plant fibre degrading bacteria) alongside nine other bacterial groups. Marked bacterial microbiome differences also occurred across phases of the population cycle. Bacterial microbiomes were lower in richness and compositionally distinct in the peak compared to the increase or decline phases of the population cycle. Direct measures of host physiology and diet quality (faecal fibre contents) most strongly supported food resource availability as a mechanism underlying phase-based differences in bacterial communities, but faecal fibre contents could not fully account for bacterial microbiome variation across phases.
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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 | 0.001 | 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".