The Afrotropical breeding grounds of the Palearctic-African migratory painted lady butterflies ( <i>Vanessa cardui</i> )
Bibliographic record
Abstract
Migratory insects are key players in ecosystem functioning and services, but their spatiotemporal distributions are typically poorly known. Ecological niche modeling (ENM) may be used to predict species seasonal distributions, but the resulting hypotheses should eventually be validated by field data. The painted lady butterfly ( Vanessa cardui ) performs multigenerational migrations between Europe and Africa and has become a model species for insect movement ecology. While the annual migration cycle of this species is well understood for Europe and northernmost Africa, it is still unknown where most individuals spend the winter. Through ENM, we previously predicted suitable breeding grounds in the subhumid regions near the tropics between November and February. In this work, we assess the suitability of these predictions through i) extensive field surveys and ii) two-year monitoring in six countries: a large-scale monitoring scheme to study butterfly migration in Africa. We document new breeding locations, year-round phenological information, and hostplant use. Field observations were nearly always predicted with high probability by the previous ENM, and monitoring demonstrated the influence of the precipitation seasonality regime on migratory phenology. Using the updated dataset, we built a refined ENM for the Palearctic-African range of V. cardui . We confirm the relevance of the Afrotropical region and document the missing natural history pieces of the longest migratory cycle described in butterflies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".