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Windborne long-distance migration of malaria mosquitoes in the Sahel

Nature volume 574, pages 404–408 (2019)Cite this article

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Abstract

Over the past two decades efforts to control malaria have halved the number of cases globally, yet burdens remain high in much of Africa and the elimination of malaria has not been achieved even in areas where extreme reductions have been sustained, such as South Africa1,2. Studies seeking to understand the paradoxical persistence of malaria in areas in which surface water is absent for 3–8 months of the year have suggested that some species of Anopheles mosquito use long-distance migration3. Here we confirm this hypothesis through aerial sampling of mosquitoes at 40–290 m above ground level and provide—to our knowledge—the first evidence of windborne migration of African malaria vectors, and consequently of the pathogens that they transmit. Ten species, including the primary malaria vector Anopheles coluzzii, were identified among 235 anopheline mosquitoes that were captured during 617 nocturnal aerial collections in the Sahel of Mali. Notably, females accounted for more than 80% of all of the mosquitoes that we collected. Of these, 90% had taken a blood meal before their migration, which implies that pathogens are probably transported over long distances by migrating females. The likelihood of capturing Anopheles species increased with altitude (the height of the sampling panel above ground level) and during the wet seasons, but variation between years and localities was minimal. Simulated trajectories of mosquito flights indicated that there would be mean nightly displacements of up to 300 km for 9-h flight durations. Annually, the estimated numbers of mosquitoes at altitude that cross a 100-km line perpendicular to the prevailing wind direction included 81,000 Anopheles gambiae sensu stricto, 6 million A. coluzzii and 44 million Anopheles squamosus. These results provide compelling evidence that millions of malaria vectors that have previously fed on blood frequently migrate over hundreds of kilometres, and thus almost certainly spread malaria over these distances. The successful elimination of malaria may therefore depend on whether the sources of migrant vectors can be identified and controlled.

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Fig. 1: Flight altitude, seasonality, wind speed and abundance of migratory anopheline species.
Fig. 2: Backwards trajectories of the flights for each Anopheles capture event.

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Data availability

Data on anopheline capture, identification, sex and gonotrophic status are available from www.boldsystems.org (project code MALAN) and in GenBank (MK585944–MK586043, inclusive).

Code availability

SAS code used for statistical analyses (and data manipulations) and 9-h backward trajectories data for each mosquito-capture event (based on HYSPLIT) are available from the corresponding author upon request. The code for plotting trajectories is available at https://github.com/benkraj/anopheles-migration.

References

  1. WHO. World Malaria Report 2017. (WHO, 2018).

  2. Gething, P. W. et al. Mapping Plasmodium falciparum mortality in Africa between 1990 and 2015. N. Engl. J. Med. 375, 2435–2445 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dao, A. et al. Signatures of aestivation and migration in Sahelian malaria mosquito populations. Nature 516, 387–390 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fontenille, D. et al. High annual and seasonal variations in malaria transmission by anophelines and vector species composition in Dielmo, a holoendemic area in Senegal. Am. J. Trop. Med. Hyg. 56, 247–253 (1997).

    Article  CAS  PubMed  Google Scholar 

  5. Fontenille, D. et al. Four years’ entomological study of the transmission of seasonal malaria in Senegal and the bionomics of Anopheles gambiae and A. arabiensis. Trans. R. Soc. Trop. Med. Hyg. 91, 647–652 (1997).

    Article  CAS  PubMed  Google Scholar 

  6. Lehmann, T. et al. Aestivation of the African malaria mosquito, Anopheles gambiae in the Sahel. Am. J. Trop. Med. Hyg. 83, 601–606 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Simard, F., Lehmann, T., Lemasson, J. J., Diatta, M. & Fontenille, D. Persistence of Anopheles arabiensis during the severe dry season conditions in Senegal: an indirect approach using microsatellite loci. Insect Mol. Biol. 9, 467–479 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Omer, S. M. & Cloudsley-Thompson, J. L. Dry season biology of Anopheles gambiae Giles in the Sudan. Nature 217, 879–880 (1968).

    Article  ADS  Google Scholar 

  9. Mamai, W. et al. Monitoring dry season persistence of Anopheles gambiae s.l. populations in a contained semi-field system in southwestern Burkina Faso, West Africa. J. Med. Entomol. 53, 130–138 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Yaro, A. S. et al. Dry season reproductive depression of Anopheles gambiae in the Sahel. J. Insect Physiol. 58, 1050–1059 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chapman, J. W., Reynolds, D. R. & Wilson, K. Long-range seasonal migration in insects: mechanisms, evolutionary drivers and ecological consequences. Ecol. Lett. 18, 287–302 (2015).

    Article  PubMed  Google Scholar 

  12. Service, M. W. Mosquito (Diptera: Culicidae) dispersal—the long and short of it. J. Med. Entomol. 34, 579–588 (1997).

    Article  CAS  PubMed  Google Scholar 

  13. Service, M. W. Mosquito Ecology Field Sampling Methods (Elsevier Applied Science, 1993).

  14. Costantini, C. et al. Density, survival and dispersal of Anopheles gambiae complex mosquitoes in a West African Sudan savanna village. Med. Vet. Entomol. 10, 203–219 (1996).

    Article  CAS  PubMed  Google Scholar 

  15. Touré, Y. T. et al. Mark-release-recapture experiments with Anopheles gambiae s.l. in Banambani Village, Mali, to determine population size and structure. Med. Vet. Entomol. 12, 74–83 (1998).

    Article  PubMed  Google Scholar 

  16. Garrett-Jones, C. The possibility of active long-distance migrations by Anopheles pharoensis Theobald. Bull. World Health Organ. 27, 299–302 (1962).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Sellers, R. F. Weather, host and vector—their interplay in the spread of insect-borne animal virus diseases. J. Hyg. (Lond.) 85, 65–102 (1980).

    Article  CAS  Google Scholar 

  18. Glick, P. A. The Distribution of Insects, Spiders, and Mites in the Air. Technical Bulletin No. 673 (US Department of Agriculture, 1939).

  19. Reynolds, D. R. et al. Atmospheric transport of mosquitoes in northeast India. Med. Vet. Entomol. 10, 185–186 (1996).

    Article  CAS  PubMed  Google Scholar 

  20. Kyalo, D. et al. A geo-coded inventory of anophelines in the Afrotropical Region south of the Sahara: 1898–2016. Welcome Open Res. 2, 57 (2017).

    Article  Google Scholar 

  21. Beier, J. C. et al. Characterization of malaria transmission by Anopheles (Diptera: Culicidae) in western Kenya in preparation for malaria vaccine trials. J. Med. Entomol. 27, 570–577 (1990).

    Article  CAS  PubMed  Google Scholar 

  22. Antonio-Nkondjio, C. et al. Complexity of the malaria vectorial system in Cameroon: contribution of secondary vectors to malaria transmission. J. Med. Entomol. 43, 1215–1221 (2006).

    Article  PubMed  Google Scholar 

  23. Toure, Y. T. et al. Perennial transmission of malaria by the Anopheles gambiae complex in a north Sudan Savanna area of Mali. Med. Vet. Entomol. 10, 197–199 (1996).

    Article  CAS  PubMed  Google Scholar 

  24. Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).

    Article  ADS  Google Scholar 

  25. Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): a metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).

    Article  Google Scholar 

  26. Hay, S. I., Rogers, D. J., Toomer, J. F. & Snow, R. W. Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, Internet access and review. Trans. R. Soc. Trop. Med. Hyg. 94, 113–127 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Nicholson, S. E. The West African Sahel: a review of recent studies on the rainfall regime and its interannual variability. ISRN Meteorol. 2013, 453521 (2013).

    Article  Google Scholar 

  28. Wilson, K. in Insect Migration: Tracking Resources through Space and Time (eds Drake, V. A. & Gatehouse, A. G.) 243–264 (Cambridge Univ. Press, 1995).

  29. Pedgley, D. E., Reynolds, D. R. & Tatchell, G. M. in Insect Migration: Tracking Resources through Space and Time (eds Drake, V. A. & Gatehouse, A. G.) 3–30 (Cambridge Univ. Press, 1995).

  30. Frean, J., Brooke, B., Thomas, J. & Blumberg, L. Odyssean malaria outbreaks in Gauteng Province, South Africa, 2007–2013. S. Afr. Med. J. 104, 335–338 (2014).

    Article  PubMed  Google Scholar 

  31. Lehmann, T. et al. Tracing the origin of the early wet-season Anopheles coluzzii in the Sahel. Evol. Appl. 10, 704–717 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lehmann, T. et al. Seasonal variation in spatial distributions of Anopheles gambiae in a Sahelian village: evidence for aestivation. J. Med. Entomol. 51, 27–38 (2014).

    Article  PubMed  Google Scholar 

  33. Huestis, D. L. et al. Seasonal variation in metabolic rate, flight activity and body size of Anopheles gambiae in the Sahel. J. Exp. Biol. 215, 2013–2021 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Fritz, G. N., Fritz, A. H. & Vander Meer, R. K. Sampling high-altitude and stratified mating flights of red imported fire ant. J. Med. Entomol. 48, 508–512 (2011).

    Article  PubMed  Google Scholar 

  35. Fanello, C., Santolamazza, F. & della Torre, A. Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Med. Vet. Entomol. 16, 461–464 (2002).

    Article  CAS  PubMed  Google Scholar 

  36. Scott, J. A., Brogdon, W. G. & Collins, F. H. Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am. J. Trop. Med. Hyg. 49, 520–529 (1993).

    Article  CAS  PubMed  Google Scholar 

  37. Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Simon, C. et al. Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).

    Article  CAS  Google Scholar 

  39. Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).

    CAS  PubMed  Google Scholar 

  40. Linton, Y.-M. et al. Mosquitoes of eastern Amazonian Ecuador: biodiversity, bionomics and barcodes. Mem. Inst. Oswaldo Cruz 108, 100–109 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bass, C. et al. PCR-based detection of Plasmodium in Anopheles mosquitoes: a comparison of a new high-throughput assay with existing methods. Malar. J. 7, 177 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Demas, A. et al. Applied genomics: data mining reveals species-specific malaria diagnostic targets more sensitive than 18S rRNA. J. Clin. Microbiol. 49, 2411–2418 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Steenkeste, N. et al. Towards high-throughput molecular detection of Plasmodium: new approaches and molecular markers. Malar. J. 8, 86 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Kent, R. J. & Norris, D. E. Identification of mammalian blood meals in mosquitoes by a multiplexed polymerase chain reaction targeting cytochrome b. Am. J. Trop. Med. Hyg. 73, 336–342 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. SAS. SAS for Windows version 9.3 https://www.sas.com/ (2011).

  46. Hu, G. et al. Mass seasonal bioflows of high-flying insect migrants. Science 354, 1584–1587 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Drake, V. A. & Reynolds, D. R. Radar Entomology: Observing Insect Flight and Migration (CABI International, 2012).

  48. Reynolds, D., Chapman, J. & Stewart, A. Windborne migration of Auchenorrhyncha (Hemiptera) over Britain. Eur. J. Entomol. 114, 554–564 (2017).

    Article  Google Scholar 

  49. Taylor, L. R. Insect migration, flight periodicity and the boundary layer. J. Anim. Ecol. 43, 225–238 (1974).

    Article  Google Scholar 

  50. Chapman, J. W., Drake, V. A. & Reynolds, D. R. Recent insights from radar studies of insect flight. Annu. Rev. Entomol. 56, 337–356 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Kaufmann, C. & Briegel, H. Flight performance of the malaria vectors Anopheles gambiae and Anopheles atroparvus. J. Vector Ecol. 29, 140–153 (2004).

    PubMed  Google Scholar 

  52. Snow, W. F. Field estimates of the flight speed of some West African mosquitoes. Ann. Trop. Med. Parasitol. 74, 239–242 (1980).

    Article  CAS  PubMed  Google Scholar 

  53. Eagles, D., Walker, P. J., Zalucki, M. P. & Durr, P. A. Modelling spatio-temporal patterns of long-distance Culicoides dispersal into northern Australia. Prev. Vet. Med. 110, 312–322 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Stefanescu, C., Alarcón, M. & Àvila, A. Migration of the painted lady butterfly, Vanessa cardui, to north-eastern Spain is aided by African wind currents. J. Anim. Ecol. 76, 888–898 (2007).

    Article  PubMed  Google Scholar 

  55. Klausner, Z., Fattal, E. & Klement, E. Using synoptic systems’ typical wind trajectories for the analysis of potential atmospheric long-distance dispersal of lumpy skin disease virus. Transbound. Emerg. Dis. 64, 398–410 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Article  ADS  PubMed  Google Scholar 

  57. Pedgley, D. E. Windborne Pests and Diseases: Meteorology of Airborne Organisms (Ellis Horwood, 1982).

  58. Gillies, M. T. & Wilkes, T. J. Field experiments with a wind tunnel on the flight speed of some west African mosquitoes (Diptera: Culicidae). Bull. Entomol. Res. 71, 65 (1981).

    Article  Google Scholar 

  59. Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R J. 5, 144–161 (2013).

    Article  Google Scholar 

  60. Hijmans, R. J. geosphere: spherical trigonometry. R package version 1.5-10 https://cran.r-project.org/package=geosphere (2017).

  61. Slowikowski, K. ggrepel: automatically position non-overlapping text labels with ‘ggplot2’. R package version 0.8.1 https://cran.r-project.org/package=ggrepel (2018).

  62. Santos Baquero, O. ggsn: north symbols and scale bars for maps created with ‘ggplot2’ or ‘ggmap’. R package version 0.5.2 https://cran.r-project.org/package=ggsn (2019).

  63. Arnold, J. B. ggthemes: extra themes, scales and geoms for ‘ggplot2’. R package version 4.2.0 https://cran.r-project.org/package=ggthemes (2019).

  64. Grolemund, G. & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40, 1–25 (2011).

    Article  Google Scholar 

  65. R Studio Team. RStudio: integrated development environment for R. R package version 3.6.0 https://www.rstudio.com/ (2015).

  66. R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/ (2016).

  67. Fisher, N. I. Statistical Analysis of Circular Data (Cambridge Univ. Press, 1993).

  68. Wickham, H. et al. ggplot2: create elegant data visualisations using the grammar of graphics. R package version 3.2.1 https://cran.r-project.org/web/packages/ggplot2/index.html (2019).

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Acknowledgements

We thank the residents of Thierola, Siguima, Markabougou and Dallowere for their consent to work near their homes, and for their wonderful assistance and hospitality; M. Keita, B. Coulibaly and O. Kone for their valuable technical assistance with field and laboratory operations; G. Fritz for consultation on the aerial sampling method using sticky panels; D. Sakai, S. F. Traore, J. Anderson, T. Wellems, M. Sullivan and S. Moretz for logistical support, F. Collins and N. Lobo for support to initiate the aerial-sampling project; J. M. C. Ribeiro and A. Molina-Cruz for reading earlier versions of this manuscript and providing us with helpful suggestions; and A. Crawford and F. (F.) Ngan for conversions of the MERRA2 and ERA5 data files to HYSPLIT format. This study was primarily supported the Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health. Rothamsted Research received grant-aided support from the UK Biotechnology and Biological Sciences Research Council (BBSRC). Y.-M.L. and R.M. are supported by the US Army. Views expressed here are those of the authors, and in no way reflect the opinions of the US Army or the US Department of Defense. The USDA is an equal opportunity provider and employer. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA.

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Authors and Affiliations

  1. Laboratory of Malaria and Vector Research, NIAID, NIH, Rockville, MD, USA

    Diana L. Huestis, Asha Krishna, Laura Veru, Benjamin J. Krajacich, Roy Faiman, Jenna Florio & Tovi Lehmann

  2. Malaria Research and Training Center (MRTC), Faculty of Medicine, Pharmacy and Odonto-stomatology, University of Bamako, Bamako, Mali

    Adama Dao, Moussa Diallo, Zana L. Sanogo, Djibril Samake, Alpha S. Yaro & Yossi Ousman

  3. Faculte des Sciences et Techniques, Universite des Sciences des Techniques et des Technologies de Bamako (FSTUSTTB), Bamako, Mali

    Alpha S. Yaro

  4. Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, Suitland, MD, USA

    Yvonne-Marie Linton & Reed Mitchell

  5. Department of Entomology, Smithsonian Institution, National Museum of Natural History, Washington, DC, USA

    Yvonne-Marie Linton & Lourdes Chamorro

  6. Centre for Ecology and Conservation, University of Exeter, Penryn, UK

    Jason W. Chapman

  7. College of Plant Protection, Nanjing Agricultural University, Nanjing, China

    Jason W. Chapman

  8. Environment and Sustainability Institute, University of Exeter, Penryn, UK

    Jason W. Chapman

  9. Natural Resources Institute, University of Greenwich, Chatham, UK

    Don R. Reynolds

  10. Rothamsted Research, Harpenden, UK

    Don R. Reynolds

  11. Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK

    David Weetman & Martin J. Donnelly

  12. Systematic Entomology Laboratory - ARS, USDA, Smithsonian Institution National Museum of Natural History, Washington, DC, USA

    Elijah Talamas & Lourdes Chamorro

  13. Florida Department of Agriculture and Consumer Services, Department of Plant Industry, Gainesville, FL, USA

    Elijah Talamas

  14. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Ehud Strobach

  15. Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, MD, USA

    Ehud Strobach

Authors
  1. Diana L. Huestis
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  2. Adama Dao
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Contributions

The project was conceived by T.L. and D.L.H. Field methods and operations were designed by D.L.H. with input from D.R.R. and J.W.C. Fieldwork, protocol optimization, data acquisition and management, and initial processing of specimens, including tentative species identification, was performed by A.D., A.S.Y., M.D., D.S., Z.L.S. and Y.O. and subsequent processing by A.K., J.F. and L.V. with inputs from E.T. and L.C. Species identification and molecular analysis of specimens were conducted primarily by Y.-M.L., R.M., A.K. and B.J.K. with contributions by D.W., R.F. and M.J.D. Data analysis and HYSPLIT simulations were carried out by T.L. with inputs from all authors, especially R.F., B.J.K., D.R.R., J.W.C., E.S. and Y.-M.L. B.J.K. mapped simulated trajectories. The manuscript was drafted by T.L. and revised by all authors. Throughout the project, all authors have contributed key ideas that have shaped the work and the final paper.

Corresponding author

Correspondence to Tovi Lehmann.

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Peer review information Nature thanks Nora Besansky, Simon Hay and Daniel Neafsay for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Study area and aerial sampling effort.

a, Map of the study area, showing aerial-sampling villages, as well as the number of sampling nights per village. Schematic map of Africa, showing the Sahel region. The base map was generated using the ggplot2 package in R68, under a GPL-2 license. b, Nightly sampling effort by year. The extension of the axes under zero indicates the sampling nights (by village), and the needles denote the total number of mosquitoes per night (regardless of the number of collecting panels per night). Dry and wet seasons are indicated by yellow and green, respectively, in the key under the x axis.

Extended Data Fig. 2 Regularity of migratory flights, flight altitude and variability among years and localities in the aerial presence of species.

a, Relationship between mosquito presence (fraction of positive nights) and the mean density of mosquitoes on collecting panels, to evaluate whether appearance can be accounted by overall abundance rather than by unique migratory nights. b, The relationship between the height of the collecting panel and mean density of mosquitoes per panel (×103; the regression line with shading denotes the 95% confidence interval of the mean), showing the mean density of mosquitoes per panel by species. The inset summarizes the covariance analysis that underlies this regression, which includes the species and height of the collecting panel. The number of nights per collecting-panel height is given in blue along the x axis (Fig. 1a). agl, above ground level. c, Variation in mosquito presence (fraction of positive nights) by species between years (top) and villages (bottom), with their 95% confidence interval. Sampling effort, expressed as the number of collecting panels per year or village, is shown adjacent to the key.

Extended Data Fig. 3 Photograph showing a tethered sticky-panel setup and attachment.

A sticky panel (3 × 1-m net) on a test helium balloon (of a lower volume and capacity), showing the attachment of the net covered with glue to the cord that tethers the balloon to the ground. The four carbon poles and Velcro attachment points are shown. A close-up image of the attachment of the panel to the cord, and an image of preparations to launch a standard 3-m balloon, are also shown.

Extended Data Table 1 Variation in the rate of mosquito capture between years, localities and heights above ground
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Huestis, D.L., Dao, A., Diallo, M. et al. Windborne long-distance migration of malaria mosquitoes in the Sahel. Nature 574, 404–408 (2019). https://doi.org/10.1038/s41586-019-1622-4

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