Assessing the potential global distribution of <i>Monochamus sutor</i> (Coleoptera: Cerambycidae) under the influence of climate change and human activities based on Maximum Entropy model
Bibliographic record
Abstract
Monochamus sutor, an important phytophagous pest, is a known vector insect of Bursaphelenchus mucronatus in addition to feeding directly on trees. Although B. mucronatus causes relatively minor damage in European and Asian forests, its threat to coniferous forests is similar to that of Bursaphelenchus xylophilus. Given that B. xylophilus evolved into a destructive pathogen after its introduction into Asia, B. mucronatus may also pose a potential threat to North American coniferous forests. Therefore, we assessed the potential global distributions areas of M. sutor and their relative dynamics under different climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) in the current (i. considering only bioclimatic factors; ii. including anthropogenic factors) and in the future (2050s and 2070s) using an optimized Maximum Entropy ecological niche model. The mean area under the curve value of the optimized model was greater than 0.86 and the true skill statistic value was greater than 0.79. Potentially suitable habitat for M. sutor is driven by a combination of temperature (Bio1 and Bio2), precipitation (Bio14, Bio15, and Bio18), and human activities. In the current period, suitable areas are concentrated in Europe, East Asia, and North America, and are smaller in the presence of anthropogenic disturbance than in the presence of bioclimatic factors alone. At the same time, under future climate scenarios, the potential range of M. sutor will always expand more than contract, with a projected increase of 1,329.02 to 1,798.23 × 104 km2 compared to the current time period, especially spread toward Canada and the United States of America in North America. The present study provides important insights into the potential risks of M. sutor, which is important to help guide decision-making in pest control as well as forest conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".