There and back again: Combining hydrogen and strontium isotopes refines the trans-Saharan migratory patterns of the butterfly Vanessa cardui
Bibliographic record
Abstract
Migratory insects serve an important role to ecosystems and economies as they participate in the long-distance transfer of nutrients, pollen, and biomass. However, migratory insects are understudied, especially compared to birds and mammals, partially because traditional tracking techniques (e.g. mark-recapture, biologgers) are often ineffective for insects because insects are small, short-lived, and numerous. Thus, isotope geolocation has become an effective tool for studying dispersing insects. The painted lady butterfly (Vanessa cardui (L.)) is a virtually cosmopolitan species that was recently found to make regular, annual multi-generational migrations across the Sahara Desert. Previous work geolocating painted ladies with hydrogen isotopes has shown early spring migratory movements of painted ladies from sub-Saharan Africa to Mediterranean Europe and autumn movements to the sub-Sahara from Europe. However, these previous works using hydrogen isotopes were unable to offer refined estimates of natal origin due to the inherent limitations of the technique. Here, we update previous hydrogen isotope-based geographic assignment by (1) using an updated model of hydrogen isotope variations across the landscape (i.e., isoscape), (2) combining with strontium isotope-based geographic assignment, and (3) expanding the number of geolocated butterflies to include capture locations on both sides of the Sahara Desert across different years. Using this method, we spatially refine previous estimates of the natal origins of successful trans-Saharan migrants and estimate the distances that successful migrants travelled. Overall, this study demonstrates the advantages of combining hydrogen and strontium isotopes for the geographic assignment of migratory butterflies and further advances our understanding of long-distance insect migration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".