Continuous‐surface geographic assignment of migratory animals using strontium isotopes: A case study with monarch butterflies
Bibliographic record
Abstract
Abstract Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) have shown promise for tracing the geographic origin of animal tissues because they have high‐resolution and show discrete spatial patterns independent and complementary to those of light isotopes. In this study, we provide a complete quantitative framework to apply ⁸⁷Sr/⁸⁶Sr for tracking migratory animals using the eastern North American population of monarch butterflies Danaus plexippus as a case study. To enable continuous‐surface geographic assignment using ⁸⁷Sr/⁸⁶Sr, we recommend following five key steps: (a) assessing feasibility, (b) sample collection, (c) laboratory analysis, (d) modelling the isoscape and (e) geographic assignment. We provide a detailed outline of these steps and then focus on steps 3–5 for the case study. For monarchs, using an extensive plant ⁸⁷Sr/⁸⁶Sr dataset ( n = 400), geospatial data and a machine learning approach, we first calibrate a regional, high‐resolution ⁸⁷Sr/⁸⁶Sr isoscape (i.e. a baseline for ⁸⁷Sr/⁸⁶Sr assignment) over their eastern North American summer breeding range. We then use the ⁸⁷Sr/⁸⁶Sr isoscape to estimate the posterior probability surface of natal origin for 100 monarchs of unknown origin. Our results demonstrate that ⁸⁷Sr/⁸⁶Sr can greatly improve the precision of isotope‐based geographic assignment. Furthermore, combining δ 2 H and ⁸⁷Sr/⁸⁶Sr into a dual assignment provides the most constrained area of natal origin. We provide a framework for ecologists and palaeoecologists to apply ⁸⁷Sr/⁸⁶Sr‐based geographic assignments for animal movement studies using contemporary or archived samples. The addition of the ⁸⁷Sr/⁸⁶Sr assignment tool will enhance our ability to study migration and dispersal in a wide variety of animals.
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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".