Why Do Cryptic Species Tend Not to Co-Occur? A Case Study on Two Cryptic Pairs of Butterflies
Bibliographic record
Abstract
As cryptic diversity is being discovered, mostly thanks to advances in molecular techniques, it is becoming evident that many of these taxa display parapatric distributions in mainland and that they rarely coexist on islands. Genetic landscapes, haplotype networks and ecological niche modeling analyses were performed for two pairs of non-sister cryptic butterfly species, Aricia agestis-A. cramera and Polyommatus icarus-P. celina (Lycaenidae), to specifically assess non-coexistence on western Mediterranean islands, and to test potential causes producing such chequered distribution patterns. We show that the morphologically and ecologically equivalent pairs of species do not coexist on any of the studied islands, although nearly all islands are colonized by one of them. According to our models, the cryptic pairs displayed marked climatic preferences and 'precipitation during the driest quarter' was recovered as the most important climatic determinant. However, neither dispersal capacity, nor climatic or ecological factors fully explain the observed distributions across particular sea straits, and the existence of species interactions resulting in mutual exclusion is suggested as a necessary hypothesis. Given that the studied species are habitat generalists, feeding on virtually unlimited resources, we propose that reproductive interference, together with climatic preferences, sustain density-dependent mechanisms like "founder takes all" and impede coexistence on islands. Chequered distributions among cryptic taxa, both sister and non-sister, are common in butterflies, suggesting that the phenomenon revealed here could be important in determining biodiversity patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".