Meta‐barcoding insights into the spatial and temporal dietary patterns of the threatened Asian Great Bustard (<i>Otis tarda dybowskii</i>) with potential implications for diverging migratory strategies
Bibliographic record
Abstract
Abstract Food resources are often not sufficient to satisfy the nutritional and energetic requirements during winter conditions at high latitudes. Dietary analysis is a prerequisite to fully understanding the feeding ecology of a species and the nature of trophic interactions. Previous dietary studies of Asian Great Bustard (Otis tarda dybowskii) relied on behavioral observations, resulting in categorization of diet limited to broad taxonomic groupings. Here, we applied a high‐throughput sequencing meta‐barcoding approach to quantify the diet of resident and migratory Asian Great Bustard in three wintering sites during early winter and late winter. We detected 57 unique plant taxa in the bustard diet, among which 15 species were confirmed by a local plant database we generated. Both agricultural and natural foods were detected, indicating a relatively broad dietary niche. Spatiotemporal dietary changes were discovered, revealing diet differences among wintering sites and a general shift toward lower plant diversity later in winter. For the nonmigratory population, we detected a significantly more diverse array of plant species in their diet. We hypothesize that dietary variation between resident and migratory populations could be involved in the recent transition to partial migration in this species, although climate change can not be excluded. Collectively, these results support protecting unharvested grain fields and naturally unplowed lands to help conserve and promote population growth of Asian Great Bustard.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".