Advances in Linking Wintering Migrant Birds to Their Breeding-Ground Origins Using Combined Analyses of Genetic and Stable Isotope Markers
Bibliographic record
Abstract
An enduring problem in avian ecology and conservation is linking breeding and wintering grounds of migratory species. As migratory species and populations vary in the degree to which individuals from distinct breeding locales mix on stop-over sites and wintering grounds, establishing migratory connectivity informs our understanding of population demography and species management. We present a new Bayesian approach for inferring breeding grounds of wintering birds of unknown origins in North America. We incorporate prior information from analysis of genetic markers into geographic origin assignment based upon stable-hydrogen isotope analysis of feathers (δ(2)H(f)), using the Loggerhead Shrike (Lanius ludovicianus). Likely geographic origins derived from analyses of DNA microsatellites were used as priors for Bayesian analyses in which birds were assigned to a breeding-ground origin using their δ(2)H(f) values. As with most applications of Bayesian methods, our approach greatly improved the results (i.e. decreased the size of the potential area of origin). Area of origin decreased by 3 to 5-fold on average, but ranged up to a 10-fold improvement. We recommend this approach in future studies of migratory connectivity and suggest that our methodology could be applied more broadly to the study of dispersal, sources of productivity of migratory populations, and a range of evolutionary phenomena.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".