Winter diet of five sympatric ungulates in west-central Alberta, Canada—inference from DNA metabarcoding of fecal pellets
Bibliographic record
Abstract
Diet is one of the determinants of ungulate ecology. However, there have been few studies of the diet of sympatric ungulates in western Canada. We used DNA metabarcoding of fecal samples to evaluate the winter diet of white-tailed deer ( Odocoileus virginianus), mule deer ( O. hemionus), moose ( Alces americanus), elk ( Cervus canadensis), and caribou ( Rangifer tarandus caribou) in west-central Alberta. We found that forbs comprised 53%–82% of the reads from plant DNA, while mosses, sedges, grasses, and shrubs made up relatively little of each ungulate’s diet. There was considerable overlap in the winter diet of the five ungulates (Horn-Morisita index = 42%–64% overlap; Bray-Curtis index = 20%–45% overlap). Moose had the most distinct diet, while elk and caribou had the least distinct. However, our analysis was restricted to a coarse taxonomic resolution (family). Lichen species were not identifiable, beyond their fungal component and a number of taxonomic assignments were difficult to rationalise given our knowledge of the diet of the five ungulate species and the ecology of west-central Alberta, Canada. DNA metabarcoding of fecal samples is efficient, but apparent limitations with the technique restrict the description and quantification of diet of the five species of ungulate that we studied.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".