A deep population stratification and ongoing local adaptations of the range-expanding lovebug Plecia longiforceps
Bibliographic record
Abstract
Rapidly range-expanding species provide a superb opportunity to study dynamic demography and ongoing adaptations to novel environments. Plecia longiforceps, a species of lovebug flies native to southeast China and Taiwan, has recently been expanding in Okinawa in Japan and Seoul in South Korea, where massive outbreaks are garnering public attention. Here we analyzed whole-genome sequences of 150 individuals across China, Korea, Okinawa, and Taiwan, including a newly identified northern Chinese population from Qingdao. We found a deep divergence between continental (Korea-China) and insular (Okinawa-Taiwan) populations and specify northern China and Taiwan as sources for Korea and Okinawa, respectively. Severely reduced genetic diversity in Korea and Qingdao suggests serial founder events during their northward expansion. An old split time between Qingdao and Korea (~3,500 generations ago) and multiple gene flows into Qingdao reject a simple scenario of a single recent northward migration. Selection signals in northern groups include genes associated with pigmentation, thermal sensing, lipid metabolism, and immunity. We observed a markedly reduced genetic diversity on X chromosome in northern populations, indicating ongoing strong sex-biased selection. Our findings portrait details of the northward range expansion in the species and highlight the role of adaptation for successful invasion at higher latitudes.
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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 | 0.000 | 0.000 |
| 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".