Integrating host plants and a key natural enemy into MaxEnt improves global suitability predictions for <i>Semanotus bifasciatus</i>
Bibliographic record
Abstract
Abstract BACKGROUND Semanotus bifasciatus is a major conifer pest that causes severe wood damage. The parasitoid Sclerodermus guani is its important natural enemy. However few studies have jointly considered host plants and enemy effects when predicting pest ranges. We applied the optimized MaxEnt model and, on this basis, constructed two models: the “host–pest” model and the “host–pest–enemy” model, to predict the potential global distribution of S. bifasciatus under future scenarios and to explore the effects of climate change and the introduction of biotic interactions. RESULTS Results showed that the climate-only model projected 2.73 × 10□ km² of suitable area under the historical climate condition, concentrated in Asia, North America and Europe, with expansion toward higher latitudes. The expansion of host plants further enhanced pest habitat suitability, nearly doubling the predicted range (5.46 × 10□ km²) and increasing the mean suitability. Moreover, the potential distribution of S. guani overlapped extensively with S. bifasciatus , reducing the total suitable area of S. bifasciatus by up to 2.67 × 10□ km², and mean suitability declined by nearly 40%, indicating effective suppression of pest risk. Centroid shifts were consistently northward, though magnitude and fragmentation varied among models. CONCLUSION Integrating host availability and enemy suppression improves the realism of distribution forecasts for S. bifasciatus . The study highlights the roles of biotic factors in shaping pest suitability and identifies potential future high-risk regions of infestation. These insights provide a solid scientific foundation for targeted monitoring, and the strategic application of biological control in adaptive forest pest management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".