Identifying nesting grounds for juvenile migratory birds with dual isotope: an initial test using North American raptors
Bibliographic record
Abstract
Abstract Understanding seasonal mobility, population connectivity, and site fidelity is critical for managing and preserving migratory species. We investigated the potential of coupling strontium (87Sr/86Sr) and hydrogen (δ2H) isotopes in feathers for quantitatively constraining natal origin for juvenile migratory predatory birds (raptors) using a probabilistic framework. We first calibrated single‐isotope models that predict spatial isotope variability in raptor feathers (called isoscapes) by analyzing and compiling isotope data for juvenile birds with known origins in North America and comparing their values to existing isotopic baselines in precipitation (for δ2H) and bioavailable strontium (for 87Sr/86Sr). We then compared the potential of hydrogen and strontium isotopes for refining geographic assignments either independently or combined. While yielding very different probability maps, hydrogen and strontium isotopes performed equally well at constraining regions of natal origin. However, thanks to the complementarity of these isotopes, dual δ2H and 87Sr/86Sr assignments increased predictive precision by an order of magnitude compared to either isotope alone. Lastly, we used the models to predict natal origin for juveniles with unknown origins that were sampled in southwestern Idaho, USA, during their autumn migration. The precision of geographic assignments for birds with unknown origins varied. Nevertheless, dual‐isotope assignments clearly distinguished individuals born outside of southwestern Idaho, and overall, predicted geographic assignments matched breeding ranges for the study species. Quantitative dual‐isotope geographic assignments enhance our ability to predict natal range of migratory raptors and complement other methods for monitoring movement and population connectivity. Combining isotopic data from feathers with other intrinsic geochemical and genetic data, as well as extrinsic markers, such as transmitters or bands, and niche‐modeling approaches will further refine key nesting areas for migratory birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".