Projected Distributions of Two Key Vectors of Lumpy Skin Disease, <i>Aedes aegypti</i> and <i>Stomoxys calcitrans</i> , Under Climate Change
Bibliographic record
Abstract
Lumpy skin disease (LSD), a severe transboundary disease of cattle, has caused substantial economic losses worldwide. Its transmission involves multiple vector species, among which Aedes aegypti and Stomoxys calcitrans are recognized as important contributors due to their broad distribution and ecological adaptability. Modeling the global distribution of the two key vectors is essential for anticipating their potential range expansion under climate change, thereby providing a scientific basis for developing targeted surveillance and control strategies for LSD. Our ensemble models revealed distinct environmental drivers and distributional responses for A. aegypti and S. calcitrans . The distribution of A. aegypti was predicted to be primarily influenced by urban land cover as well as temperature‐related variables, especially the mean temperature of the wettest quarter (Bio8) and the mean temperature of the driest quarter (Bio9). In contrast, S. calcitrans was strongly driven by managed pasture coverage and precipitation seasonality, indicating its reliance on livestock‐associated habitats and stable moisture conditions. Under future climate scenarios, A. aegypti showed a pronounced potential for expansion into higher latitudes, while S. calcitrans exhibited range shifts toward temperate regions. Taking the 2050s (SSP1‐2.6) as an example, the percentage gain for A. aegypti reached 96.2%, while for S. calcitrans , the percentage gain reached 43.98%. Our findings highlight the importance of multiple vector assessments in predicting LSD risk under climate change. Distinct habitat shifts of A. aegypti and S. calcitrans indicate the need for differentiated control strategies in different regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".