Uropygial gland microbiota of nearctic−neotropical migrants vary with season and migration distance
Bibliographic record
Abstract
Symbiotic microbiota are important drivers of host behaviour, health, and fitness. While most studies focus on humans, model organisms, and domestic or economically important species, research investigating the role of host microbiota in wild populations is rapidly accumulating. Most studies focus on the gut microbiota; however, skin and other glandular microbiota also play an important role in shaping traits that may impact host fitness. The uropygial gland is an important source of chemical cues and harbours diverse microbes that could mediate chemical communication in birds, so determining the factors most important in shaping host microbiota should improve our understanding of microbially-mediated chemical communication. Hypothesizing that temporal, geographic, and taxonomic effects influence host microbiota, we evaluated the effects of season, migration distance, and taxonomy on the uropygial gland microbiota of 18 passerine species from 11 families. By sampling 473 birds at a single stopover location during spring and fall migration and using 16S rRNA sequencing, we demonstrate that season, followed by migration distance, had the strongest influence on uropygial gland microbial community composition. While statistically significant, taxonomic family and species had only weak effects on gland microbiota. Given that temporal effects on gland microbiota were nearly ubiquitous among the species we tested, determining the consequences of and mechanisms driving this seasonal variation are important next steps.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".