Utility of alternate stable isotope calibration equations and informative priors when estimating the breeding origin of Arctic-breeding shorebirds
Bibliographic record
Abstract
Abstract Understanding migratory connectivity is essential for monitoring and conserving Arctic-breeding shorebirds, particularly given divergent rates of decline across populations. Stable hydrogen isotope analysis of feathers offers a scalable, non-invasive method to assign breeding origin, but current applications are limited by the absence of shorebird-specific calibration equations, which associate feather isotope values with environmental isotope gradients (“isoscapes”), and by longitudinal isoscape bands in the Arctic that result in diffuse origin estimates across broad, heterogeneous regions. We evaluated the utility of alternate calibration equations and the incorporation of occupancy-based informative priors for improving breeding origin assignments. Specifically, we analyzed feather isotope data from hatch-year semipalmated sandpipers ( Calidris pusilla ) captured at staging sites in the Canadian Maritimes, as well as known-origin Arctic samples, to compare calibration equations from different reference datasets and test priors derived from eBird habitat occupancy predictions. We demonstrate that Arctic samples of known origin largely align with calibration equations based on broad passerine datasets, although incorporating Arctic samples into the calibration may correct for a marginal westward bias in origin estimates. Using occupancy-based priors that reflect habitat preferences increased precision by 18.8%, identifying ‘hotspots’ within otherwise expansive isotopic bands. We highlight the validity of existing reference datasets with broad geographic coverage and demonstrate the value of informative priors to refine origin estimates. We recommend expanding Arctic sample collections across a wider longitudinal gradient to define a shorebird-specific calibration equation and further exploring potential biases in prior data sources to improve accuracy of Arctic-breeding shorebird origin assignments.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".