The importance of long‐distance dispersal and establishment events in small insects: historical biogeography of metalmark moths (Lepidoptera, Choreutidae)
Bibliographic record
Abstract
Abstract Aim To determine the importance of different biogeographical processes (vicariance, dispersal, long‐distance dispersal and establishment or LDDE) for the current distribution of metalmark moths, a group of small insects, using a time‐calibrated molecular tree. Location Global. Methods We sampled 104 species of metalmark moths with representatives from all six major biogeographical regions of the world (Afrotropical, Australasian, Neotropical, Nearctic, Oriental and Palaearctic). The taxon sampling includes c. 20% of known species in the family and covers both subfamilies. Using an eight‐locus molecular data set and secondary calibration points, we inferred a time‐calibrated tree, which was then used for ancestral range estimation with variants of the dispersal‐extinction‐cladogenesis model (DEC), some of which incorporated the founder‐event j‐parameter for modelling of LDDEs (DECj). Results The inferred phylogeny is well resolved and in accordance with earlier works. The metalmark moth distribution is best explained with DECj models. It remains unclear what the ancestral area was. Different models differ in the number and type of events estimated – DEC models infer vicariance for several nodes where DECj models usually infer LDDEs. However, the pattern that emerges from all the analyses is that dispersal and/or LDDE over transoceanic distances such as between the Afrotropics and Australasia and the Afrotropics and Neotropics occurred several times in the evolutionary history of this group. Main conclusions Vicariance may have played an important role in the early evolution of metalmark moths, while dispersal and LDDEs mostly shaped the group's distribution later on. Based on insect flight research using aerial radars, the best mechanism for explaining how small insects cross oceans is by being adapted for exploiting atmospheric conditions as opposed to employing active flight.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".