Winter-ground microhabitat use by differently coloured phenotypes affects return rate in a long-distance migratory bird
Bibliographic record
Abstract
Abstract Migratory bird populations are declining globally at alarming rates. Non-breeding site conditions affect breeding populations, but generalising non-breeding habitat conditions over large spatial regions cannot address potential fine-scale differences across landscapes or local populations. Plumage characteristics can mediate the effects of environmental conditions on individual fitness. However, whether different phenotypes use distinctive non-breeding sites, and whether they respond to non-breeding site conditions differently remains largely unknown. Stable isotopes (δ13C, δ15N, δ2H) of inert tissues are useful to infer habitat characteristics and geographic origins where those tissues were grown. We collected winter-grown feathers from pied flycatchers (Ficedula hypoleuca) on their breeding grounds over several years from males whose dorsal plumage colouration ranged continuously from brown to black and assessed their stable isotope values as proxies of local habitat conditions. Based on feather δ2H profiles we found that browner males spent their non-breeding season in drier habitats than black males. Assignment to origin analysis shows potential regional non-breeding ground separation between differently coloured males. High within-individual repeatability of both δ13C and δ15N indicate the pied flycatcher males return yearly to similar areas. Blacker males were more likely to return to the breeding grounds after dry years compared with brown males. The opposite was found in wet years. Our study demonstrates that different phenotypes are exposed to different non-breeding site conditions which can differentially affect individual survivorship. This has important ramifications for population dynamics under predicted climate change scenarios where especially brown phenotype pied flycatcher males may be under a risk of decreasing.
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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) | 0.001 | 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".