Intra- and Interspecific Differences in Diet Quality and Composition in a Large Herbivore Community
Bibliographic record
Abstract
Species diversity in large herbivore communities is often explained by niche segregation allowed by differences in body mass and digestive morphophysiological features. Based on large number of gut samples in fall and winter, we analysed the temporal dynamics of diet composition, quality and interspecific overlap of 4 coexisting mountain herbivores. We tested whether the relative consumption of grass and browse differed among species of different rumen types (moose-type and intermediate-type), whether diet was of lower quality for the largest species, whether we could identify plant species which determined diet quality, and whether these plants, which could be "key-food-resources" were similar for all herbivores. Our analyses revealed that (1) body mass and rumen types were overall poor predictors of diet composition and quality, although the roe deer, a species with a moose-type rumen was confirmed as an "obligatory non grazer", while red deer, the largest species, had the most lignified diet; (2) diet overlap among herbivores was well predicted by rumen type (high among species of intermediate types only), when measured over broad plant groups, (3) the relationship between diet composition and quality differed among herbivore species, and the actual plant species used during winter which determined the diet quality, was herbivore species-specific. Even if diets overlapped to a great extent, the species-specific relationships between diet composition and quality suggest that herbivores may select different plant species within similar plant group types, or different plant parts and that this, along with other behavioural mechanisms of ecological niche segregation, may contribute to the coexistence of large herbivores of relatively similar body mass, as observed in mountain ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".