Development and Validation of an Environmental DNA Workflow to Monitor Mosquito Populations

Abstract

Mosquitoes are vectors of numerous pathogens that pose significant threats to human and animal health, making their monitoring crucial for effective disease control. Environmental DNA (eDNA)–based approaches enable the detection of multiple organisms in their habitats without the need for morphological identification. However, they require strict and extensive protocol optimization. In this study, we focused on the need for novel monitoring strategies, particularly ones capable of overcoming the limitations of traditional methods. Given the central role of water in the life cycle of mosquitoes, we explored whether eDNA collected from aquatic habitats could provide an effective tool for mosquito detection and taxonomic identification. Specifically, we aimed to evaluate whether an eDNA‐based approach can reliably detect multiple mosquito species and accurately discriminate among them under different community compositions. To this end, we developed and validated a protocol for an eDNA‐based detection and identification method for mosquito monitoring. First, we assessed its performance by conducting laboratory experiments on samples containing nine different combinations of larvae from five mosquito species. eDNA occupancy analysis revealed varying species‐specific detection probabilities, with Aedes albopictus showing the highest detection rate and Aedes koreicus the lowest. Overall detection probability was high at moderate to high larval densities (5 to 50 ind/L), whereas it decreased at 0.5 ind/L, indicating reduced sensitivity at very low larval concentrations. Then, we collected 165 water samples from three types of field locations and assessed the influence of ecological variables, including land use and site type, on mosquito detection probability. Our results indicate that eDNA represents a promising tool for mosquito monitoring, particularly in contexts where larval densities are not extremely low, and may support its future integration into vector surveillance and control program.