Dengue vector niche modeling with future SSP climatic scenarios over India using the machine learning-based MaxEnt...

Area of Science:

• Environmental science
• Public health
• Vector ecology

Background:

• Dengue fever is a significant mosquito-borne disease.
• Climate change is altering the geographic distribution of disease vectors.
• Understanding vector distribution is crucial for public health interventions.

Purpose of the Study:

• To develop India-wide species distribution models for Aedes aegypti and Aedes albopictus.
• To project potential future shifts in vector suitability under various climate change scenarios.
• To provide evidence for enhanced vector surveillance and control strategies.

Main Methods:

• Utilized a presence-background MaxEnt framework with high-resolution bioclimatic data.
• Compiled and quality-controlled occurrence records from GBIF.
• Screened predictors for multicollinearity and prioritized based on importance.
• Evaluated model performance using spatial block cross-validation and background sensitivity analysis.
• Projected models under CMIP6 climate scenarios (SSP126, SSP370, SSP585).

Main Results:

• Developed robust species distribution models for Aedes aegypti (AUC ≈ 0.746-0.747) and Aedes albopictus (AUC ≈ 0.721-0.741).
• Identified baseline suitability and projected potential future geographic shifts for both mosquito species.
• Model performance was stable across different background point densities and cross-validation folds.

Conclusions:

• The study provides a reproducible, spatially robust evidence base for dengue vector distribution in India.
• Findings support forward planning for vector surveillance and targeted control efforts.
• Suitability mapping serves as an environmental proxy, not a mechanistic transmission model.