bioRxivAnimal Behavior & CognitionPreprint
Determination of Aedes mosquito mating success by a rapidly-evolving female-controlled lock-and-key mechanism
Leah Houri-ZeeviiD, Madison M. WalkeriD, Jacopo RazzautiiD, Anurag SharmaiD, Hilda Amalia PasolliiD, Leslie B. VosshalliD
Abstract
Mosquitoes, the worlds deadliest animal, exemplify single-mating systems where females mate only once in their lifetime, making mate choice critically important for reproductive success and mosquito control. Despite this importance, the mechanisms of female mating control and what prevents additional matings remain poorly understood. To address this gap, we developed a dual-color fluorescent sperm system in invasive Aedes aegypti mosquitoes and quantified mating patterns, confirming that 86-96% of females mate only once. Using behavioral tracking of mating pairs, deep learning, and quantitative analysis at increasing resolution, we discovered that females actively control mating initiation through a previously undescribed behavior: genital tip elongation. This female response is triggered by rapidly evolving male genital structures, creating a precise lock-and-key mechanism that determines mating success. Comparative analysis revealed that Aedes albopictus, separated from Aedes aegypti by 35 million years of evolution, employs a similar female-controlled system. However, Aedes albopictus males uniquely bypass female control when attempting cross-species matings with Aedes aegypti females but not with conspecific females. This "lock-picking" ability, combined with the known sterility of cross-species matings, may explain how Aedes albopictus competitively displaces Aedes aegypti populations in overlapping territories. Our findings redefine mosquito reproduction as a female-controlled process and establish a quantitative framework for investigating the molecular and neurobiological mechanisms underlying mating control and species competition in these globally important disease vectors.
- organism: Arabidopsis
- assay: behavioral assay
- research focus: host-pathogen & immune response
- approach: deep learning
- Citations
- 2
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