Modelling the potential distribution and niche shift of Solenopsis invicta Buren under climate change and invasion process
Bibliographic record
Abstract
As one of the most destructive and aggressive exotic harmful species, Solenopsis invicta Buren has spread rapidly in China, posing serious threats to biodiversity as well as human production and life. To formulate effective prevention and control measures, we first compared the bioclimatic variables of S. invicta between China and the USA. Subsequently, we employed the MaxEnt model and the “ecospat” package to predict the potential distribution and niche shift of S. invicta . The similar average annual temperature and annual precipitation between China and the USA serve as crucial ecological and environmental foundations for the successful invasion of S. invicta . Under the current climate, S. invicta is primarily distributed in the eastern and southern coastal regions of China and the USA. Under future climate scenarios, the suitable habitat area for S. invicta is projected to continue increasing in China, while it is expected to decrease in the USA. Mean diurnal range (Bio2), precipitation seasonality (Bio15), and other climatic factors exhibited vital niche differentiation. The niche of S. invicta has significant shifted in both climatic and geographic spaces, while maintaining niche conservatism during the invasion process. S. invicta can effectively adapt to new habitats through niche shifts during the invasion process. It is not advisable to directly apply the prediction experiences and threshold values from the United States to guide the prevention and control of S. invicta in China in the future. Overall, the analysis provided a scientific basis for the government and local organizations to prevent and control S. invicta .
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".