Ecological Informatics (Dec 2024)
Landscape and climatic factors shaping mosquito abundance and species composition in southern Spain: A machine learning approach to the study of vector ecology
Abstract
Vector-borne diseases pose significant challenges to public health, with mosquitoes acting as crucial vectors for pathogens globally. This study explores the interaction between environmental and climate factors, investigating their influence on the abundance and species composition of mosquitoes in southwestern Spain, a region endemic to several mosquito-borne diseases.Using comprehensive field data from 2020, we analysed mosquito abundance and species richness alongside remote sensing variables and modeling techniques, including the machine learning Random Forest. We collected 5859 female mosquitoes representing 13 species. Non-linear correlations were observed between mosquito abundance and climatic variables, notably temperature and rainfall. Extremely high temperatures correlated with a decrease in mosquito abundance, while accumulated rainfall in the three weeks preceding sampling positively impacted mosquito abundance by providing breeding habitats. A positive correlation between Normalized Difference Vegetation Index (NDVI) and mosquito metrics was also found, aligning with prior studies highlighting vegetation's role shaping mosquito habitats. Interestingly, a negative relationship was observed between mosquito species richness and autumn NDVI. Additionally, wind speed negatively affected mosquito species richness.This research provides valuable insights into the ecological determinants of mosquito abundance and species composition in a Mediterranean climate. These findings are crucial for understanding disease transmission dynamics and improving vector control strategies. By integrating climatic characteristics into public health interventions, management measures can become more targeted and efficient, especially during periods of heightened temperature.
