Two ways to be endemic. Alps and Apennines are different functional refugia during climatic cycles
Bibliographic record
Abstract
Endemics co-occur because they evolved in situ and persist regionally or because they evolved ex situ and later dispersed to shared habitats, generating evolutionary or ecological endemicity centres, respectively. We investigate whether different endemicity centres can intertwine in the region ranging from Alps to Sicily, by studying their butterfly fauna. We gathered an extensive occurrence dataset for butterflies of the study area (27,123 records, 269 species, in cells of 0.5x0.5 degrees of latitude-longitude). We applied molecular-based delimitation methods (GMYC model) to 26,557 COI sequences of Western Palearctic butterflies. We identified entities based on molecular delimitations and the most recent checklist of European butterflies and objectively attributed occurrences to their most probable entity. We obtained a zoogeographic regionalisation based on the 69 endemics of the area. Using phylogenetic ANOVA we tested if endemics from different centres differ from each other and from non-endemics for key ecological traits and divergence time. Endemicity showed high incidence in the Alps and Southern Italy. The regionalisation separated the Alps from the Italian Peninsula and Sicily. The endemics of different centres showed a high turnover and differed in phenology and distribution traits. Endemics are on average younger than non-endemics and the Peninsula-Sicily endemics also have lower variance in divergence than those from the Alps. The observed variation identifies Alpine endemics as paleoendemics, now occupying an ecological centre, and the Peninsula-Sicily ones as neoendemics, that diverged in the region since the Pleistocene. The results challenge the common view of the Alpine-Apennine area as a single “Italian refugium”.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".