Phylogeography and bioclimatic models revealed a complicated genetic structure and future range shifts of <i>Lymantria monacha</i> L.
Bibliographic record
Abstract
Abstract The phylogeography of economically important forest pests is important for understanding their demographic and evolutionary history. Linking the genetic data obtained with the bioclimatic models helps reveal future demographic trends of the pest species studied. Lymantria monacha is a polyphagous species that feeds on numerous coniferous and deciduous trees throughout the Palaearctic and is known to cause catastrophic defoliation, particularly in Europe. In addition, data from various mapping programmes over the last decade have revealed changes in the distribution of L . monacha . Therefore, in this study, we decided to clarify the evolutionary and demographic history of this important forest species using genetic data complemented by bioclimatic modelling. Our results confirmed the systematic status and monophyly of L . monacha . However, the lack of a geographical pattern between the studied regions suggests that the current genetic structure may be the result of recent dispersal events. Moreover, we found that the areas of high genetic diversity are consistent with potential past range shifts and survival of changes in climate and host plant availability. These two main variables also seem to determine the future range of L . monacha . Also, our modelling confirmed a poleward shift in its range and with a significant retraction from its current southern edge of distribution.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".