Contrasting assignment of migratory organisms to geographic origins using long‐term versus year‐specific precipitation isotope maps
Bibliographic record
Abstract
Summary As a result of predictable large‐scale continental gradients in the isotopic composition of precipitation, stable isotopes of hydrogen (δ 2 H) are useful endogenous markers for delineating long‐distance movements of animals. Models to predict patterns of δ 2 H in precipitation (δ 2 H p ), and consequently determine likely geographic origin of migratory animals, have traditionally used static, amount‐weighted long‐term average values of δ 2 H p over the growing season. However, animal tissues reflect H incorporated from food webs that integrate precipitation over a single year's growing season or portions thereof. Inter‐annual variation in precipitation and other climatic variables may lead to deviations from predictions derived from long‐term mean precipitation isotopic values and could therefore lead to assignment errors for specific years and locations that are atypical. We examined whether using biologically relevant short‐term δ 2 H p isoscapes can improve estimates of geographic origin in comparison with long‐term isoscapes. Using δ 2 H data from known‐origin tissues of two migratory organisms in North America and Europe, we compared the accuracy, precision and similarity of assigned origins using both short‐ and long‐term δ 2 H p isoscapes. Relative to long‐term δ 2 H p isoscapes, using short‐term isoscapes for assignment often resulted in dissimilar regions of likely origin but did not significantly improve accuracy or precision. This was likely due to reduced spatial coverage in the data used to generate the short‐term δ 2 H p isoscapes. We suggest that continued efforts to collect precipitation isotope data with a large spatiotemporal range will benefit future research on incorporating temporal variation in the amount and isotopic composition of precipitation into geospatial assignment models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".