Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.03.10.642347

Comparison of oviposition and adult trapping to monitor wMel introgression for Wolbachia-based vector control

Nelson, E.·Pereira, T. N.·Ribeiro, E. M. d. C.·Mafra e Silva, B. D.·Camillo, C.·Rodrigues da Costa, T.·Teixeira, M. M.·Ko, A. I.·Cummings, D. A.·Moreira, L. A.
View original source ↗Export metadata ↓

Abstract

Wolbachia introgression into Aedes aegypti mosquito populations has been shown to be effective in preventing dengue and is being evaluated for WHO prequalification. Monitoring the long-term introgression of Wolbachia (wMel)-positive Aedes aegypti mosquitoes, however, requires labor-intensive and costly BG-Sentinel traps (BG-traps). More affordable alternatives, such as using oviposition traps (ovitraps), have not been fully evaluated. Ae. aegypti eggs and adults were collected from 124 ovitraps and 237 BG-traps, respectively, across 12 clusters in Belo Horizonte, Brazil from March to May 2023 as part of the EVITA Dengue trial. We used a qPCR assay to detect wMel in a sample of L3-L4 stage larvae (up to 29) that were reared from eggs in ovitraps and adults from BG-traps (up to 10 per BG-trap). We used mixed effects models to compare estimates of cluster-level wMel introgression from ovitrap and BG-trap data over time. Among 3,675 larvae reared from ovitraps, wMel prevalence was 0.50 (95% CI: 0.48-0.51). Among 1,244 adult Ae. aegypti tested from BG-traps, wMel prevalence was 0.45 (95% CI: 0.42-0.48). Cluster-level wMel introgression in larvae and adults was highly correlated (Spearmans r = 0.70, p = 6.71e-06). Multivariate analysis found that ovitrap estimates of introgression were associated with BG-trap estimates in the same month when models incorporated the previous months ovitrap wMel-positive count, the proportion of wMel in ovitraps in the current and previous month, and Ae. aegypti abundance. Leveraging this model, predicted wMel introgression from ovitrap data were highly correlated with observed introgression from BG-trap data (rs,counts=0.98, p=1.53e-14; rs,prevalences=0.82, p=0.11e-05) and provided greater precision than crude ovitrap-based estimates. These findings indicate that ovitrap-based monitoring represents a low cost, more efficient approach to evaluating introgression as the Wolbachia-based interventions are scaled up and implemented broadly in high burden regions for dengue and other arboviral diseases. Author SummaryDengue fever is a major global health burden, and one promising way to control it is by releasing Aedes aegypti mosquitoes infected with a bacteria called Wolbachia. This bacteria reduces the mosquitoes ability to spread dengue. However, monitoring the success of Wolbachia in mosquito populations over time requires expensive and labor-intensive traps. Our study explored a more affordable alternative: using oviposition traps (ovitraps), which collect mosquito eggs instead of adults. In Belo Horizonte, Brazil, we compared data from eggs collected in ovitraps with data from standard mosquito traps (BG-traps) that catch adult mosquitoes. By analyzing the mosquitoes for Wolbachia, we found that the egg-based method provided reliable estimates of Wolbachia levels in the mosquito population. These results suggest that ovitraps could be a cost-effective and efficient way to monitor Wolbachias spread. This approach could help improve dengue prevention efforts, making it easier for public health programs to track and expand this control strategy in areas where dengue is a major concern.

Source connections

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nelson, E., Pereira, T. N., Ribeiro, E. M. d. C., Mafra e Silva, B. D., Camillo, C., Rodrigues da Costa, T., Teixeira, M. M., Ko, A. I., Cummings, D. A., Moreira, L. A.. 2025-03-13. Comparison of oviposition and adult trapping to monitor wMel introgression for Wolbachia-based vector control. https://doi.org/10.1101/2025.03.10.642347

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗
bioRxiv2026

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗
bioRxiv2026

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗