A new approach to improve acoustic trapping effectiveness for Aedes aegypti (Diptera: Culicidae)

Dynamics of Aedes aegypti mating behaviour

The use of pheromones, while common, remains underexplored in mosquito research. Understanding Aedes aegypti's mating behaviour and pheromones is crucial for expanding knowledge and advancing vector control strategies. Unlike other species, Aedes mosquitoes have adaptable mating behaviour, complicating the study of their communication mechanisms. Current literature on Aedes communication is sparse, not due to lack of effort but because of its complexity. Ae. aegypti's mating behaviour is influenced by sensory cues and environmental factors. Swarming, which facilitates mating aggregation, is triggered by host odours, highlighting the role of semiochemicals alongside aggregation pheromones. Cuticular hydrocarbons may act as chemical signals in mating, though their roles are unclear. Acoustic signals significantly contribute to mate attraction and male fitness assessment, showcasing the multidimensional nature of Ae. aegypti sexual communication. Understanding these aspects can enhance targeted control strategies and reduce mosquito populations and disease transmission.

Multisensory integration in Anopheles mosquito swarms: The role of visual and acoustic information in mate tracking and collision avoidance

Male mosquitoes form aerial aggregations, known as swarms, to attract females and maximize their chances of finding a mate. Within these swarms, individuals must be able to recognize potential mates and navigate the dynamic social environment to successfully intercept a mating partner. Prior research has almost exclusively focused on the role of acoustic cues in mediating the male mosquito's ability to recognize and pursue flying females. However, the role of other sensory modalities in this behavior has not been explored. Moreover, how males avoid collisions with one another in the dense swarm while pursuing females remains poorly understood. In this study, we combined free-flight and tethered flight simulator experiments to demonstrate that swarming Anopheles coluzzii mosquitoes integrate visual and acoustic information to track conspecifics and avoid collisions. Our tethered experiments revealed that acoustic stimuli gated mosquito steering responses to visual objects simulating nearby mosquitoes, especially in males that exhibited attraction to visual objects in the presence of female flight tones. Additionally, we observed that visual cues alone could trigger changes in mosquitoes' wingbeat amplitude and frequency. These findings were corroborated by our free-flight experiments, which revealed that mosquitoes modulate their flight responses to nearby conspecifics in a similar manner to tethered animals, allowing for collision avoidance within swarms. Together, these results demonstrate that both males and females integrate multiple sensory inputs to mediate swarming behavior, and for males, the change in flight kinematics in response to multimodal cues allows them to simultaneously track females while avoiding collisions.

Outcomes from international field trials with Male Aedes Sound Traps: Frequency-dependent effectiveness in capturing target species in relation to bycatch abundance

Aedes aegypti and Aedes albopictus vector dengue, chikungunya and Zika viruses. With both species expanding their global distributions at alarming rates, developing effective surveillance equipment is a continuing priority for public health researchers. Sound traps have been shown, in limited testing, to be highly species-specific when emitting a frequency corresponding to a female mosquito wingbeat. Determining male mosquito capture rates in sound traps based on lure frequencies in endemic settings is the next step for informed deployment of these surveillance tools. We field-evaluated Male Aedes Sound Traps (MASTs) set to either 450 Hz, 500 Hz, 550 Hz or 600 Hz for sampling Aedes aegypti and/or Aedes albopictus and compared catch rates to BG-Sentinel traps within Pacific (Madang, Papua New Guinea) and Latin American (Molas, Mexico and Orange Walk Town, Belize) locations. MASTs set to 450-550 Hz consistently caught male Ae. aegypti at rates comparable to BG-Sentinel traps in all locations. A peak in male Ae. albopictus captures in MASTs set at 550 Hz was observed, with the lowest mean abundance recorded in MASTs set to 450 Hz. While significantly higher abundances of male Culex were sampled in MASTs emitting lower relative frequencies in Molas, overall male Culex were captured in significantly lower abundances in the MASTs, relative to BG-Sentinel traps within all locations. Finally, significant differences in rates at which male Aedes and Culex were positively detected in trap-types per weekly collections were broadly consistent with trends in abundance data per trap-type. MASTs at 550 Hz effectively captured both male Ae. aegypti and Ae. albopictus while greatly reducing bycatch, especially male Culex, in locations where dengue transmission has occurred. This high species-specificity of the MAST not only reduces staff-time required to sort samples, but can also be exploited to develop an accurate smart-trap system-both outcomes potentially reducing public health program expenses.

A Low-Powered and Highly Selective Trap for Male Aedes (Diptera: Culicidae) Surveillance: The Male Aedes Sound Trap

As Aedes aegypti (Linnaeus, Diptera: Culicidae) expands its global distribution and vectors a range of debilitating arboviruses there is an increased need for enhanced mosquito surveillance. Consequently, we developed a Male Aedes Sound Trap (MAST) that requires minimal power and is highly species-specific. Two different versions of the MAST were developed, one that uses synthetic pyrethroid to kill captured mosquitoes (MAST Spray) and another which has an internal divider to create a killing chamber in which a sticky panel can be placed to capture mosquitoes (MAST Sticky). We compared weekly capture rates of male Ae. aegypti and bycatch from the two MAST versions to those from BG-Sentinel (BGS) traps and Sound-producing BG-Gravid Aedes Traps (SGATs) throughout Cairns, northern Australia. Weekly mean male Ae. aegypti catches did not significantly differ between trap types. However, the rate of positive weekly detections of male Ae. aegypti was lower for the MAST Sticky than the other three trap types. The MASTs sampled significantly fewer mosquitoes other than male Ae. aegypti, than either the BGS trap or the SGAT. Also, the MASTs and SGATs all caught significantly less non-Culicidae bycatch than the BGS traps. Consequently, we have developed a versatile male Ae. aegypti trap which is potentially of great benefit to Ae. aegypti surveillance programs.

Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% (±0.55) and 94.56% (±0.77), respectively, whereas the multiclass had an accuracy of 78.12% (±2.09). The best sensitivity was observed in the ensemble approach (96.82% ± 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% ± 3.83) and the binary (88.49% ± 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% ± 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.