Tatiana M. Sulesco

The paper develops a process-based model to predict the spatial-temporal occurrence of Culex pipiens s.s./Cx. torrentium in Germany, accurately capturing phenology and overwintering behavior.

This paper evaluates using low-cost smartphone imaging with geometric wing morphometrics to accurately identify invasive Aedes mosquito species, finding it reliable but with higher observer variability compared to professional microscopes.

The paper introduces a dataset of mosquito wing images to support geometric morphometry and machine learning for species identification.