Climate Change Facilitates the Potentially Suitable Habitats of the Invasive Crop Insect Ectomyelois ceratoniae (Zeller)
Bibliographic record
Abstract
Invasive alien insects directly or indirectly driven by climate change threaten crop production and increase economic costs worldwide. Ectomyelois ceratoniae (Zeller) is a highly reproductive invasive crop insect that can severely damage fruit commodities and cause significant economic losses globally. Estimating the global potentially suitable habitats (PSH) of E. ceratoniae is an important aspect of its invasive risk assessment and early warning. Here, we constructed an optimized MaxEnt model based on the global distribution records of E. ceratoniae, and nine environmental variables (EVs), to predict its global PSH under current and future climates. Our results showed that the RM value was 2.0 and the mean area under receiver operating characteristic curve (AUC) value was 0.972, indicating the high accuracy of the optimal MaxEnt model. The mean temperature of driest quarter (bio9, 50.2%), mean temperature of wettest quarter (bio8, 16.9%), temperature seasonality (bio4, 9.7%), and precipitation of coldest quarter (bio19, 9.1%) were the significant EVs affecting its distribution patterns. The global PSH of E. ceratoniae are mainly located in western Asia under current climate scenarios (687.57 × 104 km2), which showed an increasing trend under future climate scenarios. The PSH of E. ceratoniae achieved the maximum under the shared socioeconomic pathway (SSP) 1–2.6 in the 2030s and under the SSP2-4.5 in the 2050s. The increased PSH of E. ceratoniae are mainly located in southwestern Asia, northwestern Europe, northwestern South America, northwestern North America, southern Oceania, and northwestern Africa. Our findings suggest that quarantine officials and governmental departments in the above high-risk invasion areas should strengthen monitoring and early warning to control E. ceratoniae; in particular, cultural measures should be taken in areas where its further expansion is expected in the future.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".