Enriching the isotopic toolbox for migratory connectivity analysis: a new approach for migratory species breeding in remote or unexplored areas
Bibliographic record
Abstract
Abstract Aim We examined three potential enhancements of the stable isotope technique for elucidating migratory connectivity in birds inhabiting poorly studied areas, illustrated for Eurasian cranes ( Grus grus ) that overwinter in and migrate through Israel. First, we examined the use of oxygen stable isotopes (δ 18 O), seldom applied for this purpose. Second, we examined the relationship between ambient water δ 18 O and hydrogen stable isotope (δ 2 H) values derived from various models, to determine the geographical origins of migrants. Third, we introduced the use of probabilistic distribution modelling to refine the assignment to origin of migrants lacking detailed distribution maps. Location Feather samples were collected in the Hula Valley (northern Israel) and across the species breeding range in north Eurasia. Methods We analysed δ 18 O and δ 2 H in primary and secondary flight feathers using standard mass spectrometry. The maximum entropy ( maxent ) model was used to map the probability surface of potential breeding areas, as a Bayesian prior for assigning Hula Valley cranes to potential breeding grounds. Results We found that δ 18 O was suitable and informative. The soil water isoscape performed better for δ 18 O while precipitation isoscape was preferable for δ 2 H. The maxent ‐based probability surface largely refined assignments. Overall, most (>85%) cranes were assigned to the area west of the Ural Mountains, but for two individuals, most of the assigned area (>90%) was farther east, suggesting, for the first time, that Eurasian cranes may undertake the North Asia–Middle East (and perhaps Africa) migration flyway. Main conclusions Our results call for broader use of δ 18 O in migratory connectivity studies and for application of probabilistic distribution modelling. We also encourage investigation of factors determining δ 18 O and δ 2 H integration into animal tissues. The proposed framework may help improve our understanding of migratory connectivity of species inhabiting previously unexplored areas and thus contribute to the development of efficient conservation plans.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".