Data from: DNA metabarcoding reveals the broad and flexible diet of a declining aerial insectivore
Bibliographic record
Abstract
Aerial insectivores are highly mobile predators that feed on diverse prey items that have highly variable distributions. As such, investigating the diet, prey selection, and prey availability of aerial insectivores can be challenging. In this study, we used an integrated DNA barcoding method to investigate the diet and food supply of Barn Swallows, an aerial insectivore whose North American population has declined over the past 40 years. We tested the hypotheses that Barn Swallows are generalist insectivores when provisioning their young and select prey based on size. We predicted that the diets of nestlings would contain a range of insect taxa but would be biased towards large prey items and that the diet of nestlings would change as prey availability changed. We collected insects using Malaise traps at ten breeding sites and identified specimens using standard DNA barcoding. The sequences from these insect specimens were used to create a custom reference database of prey species and their relative sizes for our study area. We identified insect prey items from nestling fecal samples by using high-throughput DNA sequencing and comparing the sequences to our custom reference database. Barn Swallows fed nestlings prey items from 130 families representing 13 orders but showed selection for larger prey items that were predominantly from seven dipteran families. Nestling diet varied both within and between breeding seasons as well as between breeding sites. This dietary flexibility suggests that Barn Swallows are able to adjust their provisioning to changing prey availability on the breeding grounds when feeding their nestlings. Our study demonstrates the utility of custom reference databases for linking the abundance and size of insect prey in the habitat with prey consumed when employing molecular methods for dietary analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".