Windborne long-distance migration of malaria mosquitoes in the Sahel

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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.

Data availability

Data on anopheline capture, identification, sex and gonotrophic status are available from (project code MALAN) and in GenBank (MK585944MK586043, 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


  1. 1.

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

  2. 2.

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

  3. 3.

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

  4. 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).

  5. 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).

  6. 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).

  7. 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).

  8. 8.

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

  9. 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).

  10. 10.

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

  11. 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).

  12. 12.

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

  13. 13.

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

  14. 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).

  15. 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).

  16. 16.

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

  17. 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).

  18. 18.

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

  19. 19.

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

  20. 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).

  21. 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).

  22. 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).

  23. 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).

  24. 24.

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

  25. 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).

  26. 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).

  27. 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).

  28. 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. 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. 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).

  31. 31.

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

  32. 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).

  33. 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).

  34. 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).

  35. 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).

  36. 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).

  37. 37.

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

  38. 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).

  39. 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).

  40. 40.

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

  41. 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).

  42. 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).

  43. 43.

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

  44. 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).

  45. 45.

    SAS. SAS for Windows version 9.3 (2011).

  46. 46.

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

  47. 47.

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

  48. 48.

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

  49. 49.

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

  50. 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).

  51. 51.

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

  52. 52.

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

  53. 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).

  54. 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).

  55. 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).

  56. 56.

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

  57. 57.

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

  58. 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).

  59. 59.

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

  60. 60.

    Hijmans, R. J. geosphere: spherical trigonometry. R package version 1.5-10 (2017).

  61. 61.

    Slowikowski, K. ggrepel: automatically position non-overlapping text labels with ‘ggplot2’. R package version 0.8.1 (2018).

  62. 62.

    Santos Baquero, O. ggsn: north symbols and scale bars for maps created with ‘ggplot2’ or ‘ggmap’. R package version 0.5.2 (2019).

  63. 63.

    Arnold, J. B. ggthemes: extra themes, scales and geoms for ‘ggplot2’. R package version 4.2.0 (2019).

  64. 64.

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

  65. 65.

    R Studio Team. RStudio: integrated development environment for R. R package version 3.6.0 (2015).

  66. 66.

    R Core Team. R: a language and environment for statistical computing. (2016).

  67. 67.

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

  68. 68.

    Wickham, H. et al. ggplot2: create elegant data visualisations using the grammar of graphics. R package version 3.2.1 (2019).

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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.

Author information

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.

Correspondence to Tovi Lehmann.

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The authors declare no competing interests.

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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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