DNA metabarcoding allows non-invasive identification of arthropod prey provisioned to nestling Rufous hummingbirds (<i>Selasphorus rufus</i>)
Bibliographic record
Abstract
Hummingbirds consume sugars from nectar, sap and honeydew, and obtain protein, fat and minerals from arthropods. To date, the identity of arthropod taxa in hummingbird diets has been investigated by observation of foraging or examination of alimentary tract contents. Direct examination of nestling provisioning adds the extra complication of disturbance to the young and mother. Here, we show that arthropod food items provisioned to Rufous hummingbird (Selasphorus rufus) nestlings can be identified by a safe and non-invasive protocol using next-generation sequencing (NGS) of DNA from nestling fecal pellets collected post-fledging. We found that females on southern Vancouver Island (British Columbia, Canada) provisioned nestlings with a wide range of arthropod taxa. The samples examined contained three Classes, eight Orders, 48 Families, and 87 Genera, with from one to 15 Families being identified in a single pellet. Soft-bodied Dipterans were found most frequently and had the highest relative abundance; hard-bodied prey items were absent from almost all samples. Substantial differences in taxa were found within season and between years, indicating the importance of multi-year sampling when defining a prey spectrum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".