in

Tropical cyclones impact the dispersal of a globally invasive moth pest


Abstract

Wind patterns are a crucial factor for migratory insect pests. However, the impact of extreme weather events, such as tropical cyclones (TCs), on migratory pest dynamics remains unclear. Here, we combined long-term trapping data with climatic variables and hydrogen stable isotope analysis to investigate how TCs influence the population dynamics of a globally invasive moth pest, fall armyworm (Spodoptera frugiperda), across the Gulf of America, formerly known as the Gulf of Mexico. TCs significantly increased fall armyworm moth abundance, shifting their peak activity across years. Most moths originated from southeastern Florida and the Caribbean, resulting in a 54% increase in migratory moths during the TC season. These findings reveal a previously unquantified link between extreme weather events (TCs) and migratory pest outbreaks. As TCs intensify, fall armyworm migration patterns may become increasingly unpredictable, posing greater challenges for pest management and agricultural sustainability.

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Data availability

All data and code are available in the supplementary materials and/or have been archived on Figshare (Calixto and Paula-Moraes98, https://doi.org/10.6084/m9.figshare.31169626).

Code availability

The codes and packages used for data processing with standard software R described in “Methods” section, and analysis and results are also available under release Version 2 2026-01-28, 08:30 at Figshare (Calixto and Paula-Moraes98, https://doi.org/10.6084/m9.figshare.31169626).

References

  1. Johnson, C. G. Migration and Dispersal of Insects by Flight (Methuen & Co., 1969).

  2. Drake, V. A. & Farrow, R. A. The influence of atmospheric structure and motions on insect migration. Annu. Rev. Entomol. 33, 183–210 (1988).

    Google Scholar 

  3. Chapman, J. W. et al. Flight orientation behaviors promote optimal migration trajectories in high-flying insects. Science 327, 682–685 (2010).

    Google Scholar 

  4. 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).

    Google Scholar 

  5. Gao, F. et al. High migratory potential of fall armyworm in West Africa despite stable temperatures and widely available year-round habitats. Insect Sci. https://doi.org/10.1111/1744-7917.13502. (2025).

  6. Otuka, A., Huang, S.-H., Sanada-Morimura, S. & Matsumura, M. Migration analysis of Nilaparvata lugens (Hemiptera: Delphacidae) from the Philippines to Taiwan under typhoon-induced windy conditions. Appl. Entomol. Zool. 47, 263–271 (2012).

    Google Scholar 

  7. Hu, G. et al. The influence of Typhoon Khanun on the return migration of Nilaparvata lugens (Stål) in Eastern China. PLoS ONE 8, e57277 (2013).

    Google Scholar 

  8. Andraca-Gómez, G. et al. A potential invasion route of Cactoblastis cactorum within the Caribbean region matches historical hurricane trajectories. Biol. Invasions 17, 1397–1406 (2015).

    Google Scholar 

  9. Torres, J. A. Lepidoptera outbreaks in response to successional changes after the passage of Hurricane Hugo in Puerto Rico. J. Trop. Ecol. 8, 285–298 (1992).

    Google Scholar 

  10. Murata, M., Etoh, T., Itoyama, K. & Tojo, S. Sudden occurrence of the common cutworm, Spodoptera litura (Lepidoptera: Noctuidae) in southern Japan during the typhoon season. Appl. entomol. Zool. 33, 419–427 (1998).

    Google Scholar 

  11. Hu, G. et al. The East Asian insect flyway: geographical and climatic factors driving migration among diverse crop pests. Annu. Rev. Entomol. 70, 1–22 (2025).

    Google Scholar 

  12. Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Google Scholar 

  13. Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    Google Scholar 

  14. Moore, M. P., Shaich, J. & Stroud, J. T. Upslope migration is slower in insects that depend on metabolically demanding flight. Nat. Clim. Chang. 13, 1063–1066 (2023).

    Google Scholar 

  15. Porter, J. H., Parry, M. L. & Carter, T. R. The potential effects of climatic change on agricultural insect pests. Agric. For. Meteorol. 57, 221–240 (1991).

    Google Scholar 

  16. Skendžić, S., Zovko, M., Živković, I. P., Lešić, V. & Lemić, D. The impact of climate change on agricultural insect pests. Insects 12, 440 (2021).

    Google Scholar 

  17. Yang, Y. et al. Climate change exacerbates the environmental impacts of agriculture. Science 385, eadn3747 (2024).

    Google Scholar 

  18. Kropf, C. M., Vaterlaus, L., Bresch, D. N. & Pellissier, L. Tropical cyclone risk for global ecosystems in a changing climate. Nat. Clim. Chang. 15, 92–100 (2025).

    Google Scholar 

  19. ESCOP. A Science Roadmap for Food & Agriculture. Experiment Station Committee on Organization and Policy https://escop.info/roadmaptext/ (2025).

  20. Montezano, D. G. et al. Host plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. Afr. Entomol. 26, 286–300 (2018).

    Google Scholar 

  21. Yu, S. J. Insecticide resistance in the fall armyworm, Spodoptera frugiperda (J. E. Smith). Pestic. Biochem. Physiol. 39, 84–91 (1991).

    Google Scholar 

  22. Yu, S. J. Detection and biochemical characterization of insecticide resistance in fall armyworm (Lepidoptera: Noctuidae). J. Econ. Entomol. 85, 675–682 (1992).

    Google Scholar 

  23. Gutiérrez-Moreno, R. et al. Field-evolved resistance of the fall armyworm (Lepidoptera: Noctuidae) to synthetic insecticides in Puerto Rico and Mexico. J. Econ. Entomol. 112, 792–802 (2019).

    Google Scholar 

  24. Gutiérrez-Moreno, R. et al. Susceptibility of fall armyworms (Spodoptera frugiperda J.E.) from Mexico and Puerto Rico to Bt proteins. Insects 11, 831 (2020).

    Google Scholar 

  25. Rabelo, M. M., Paula-Moraes, S. V., Pereira, E. J. G. & Siegfried, B. D. Contrasting susceptibility of lepidopteran pests to diamide and pyrethroid insecticides in a region of overwintering and migratory intersection. Pest Manag. Sci. 76, 4240–4247 (2020).

    Google Scholar 

  26. Flores-Rivera, X. L., Paula-Moraes, S. V., Johnson, J. W., Jack, C. J. & Perera, O. P. Helicoverpa genus on the edge of the continental U.S.: flight phenology, analysis of hybrid presence, and insecticide performance in high-input field crops in Puerto Rico. Front. Insect Sci. 2, 1010310 (2022).

    Google Scholar 

  27. Storer, N. P. et al. Discovery and characterization of field resistance to Bt Maize: Spodoptera frugiperda (Lepidoptera: Noctuidae) in Puerto Rico. J. Econ. Entomol. 103, 1031–1038 (2010).

    Google Scholar 

  28. Huang, F. et al. Cry1F resistance in fall armyworm Spodoptera frugiperda: single gene versus pyramided Bt Maize. PLoS ONE 9, e112958 (2014).

    Google Scholar 

  29. Yang, F. et al. F2 screen, inheritance and cross-resistance of field-derived Vip3A resistance in Spodoptera frugiperda (Lepidoptera: Noctuidae) collected from Louisiana, USA. Pest Manag. Sci. 74, 1769–1778 (2018).

    Google Scholar 

  30. Yang, F., Williams, J., Porter, P., Huang, F. & Kerns, D. L. F2 screen for resistance to Bacillus thuringiensis Vip3Aa51 protein in field populations of Spodoptera frugiperda (Lepidoptera: Noctuidae) from Texas, USA. Crop Prot. 126, 104915 (2019).

    Google Scholar 

  31. Luginbill, P. The Fall Army Worm (US Department of Agriculture, 1928).

  32. Sparks, A. N. A review of the biology of the fall armyworm. Fla. Entomol. 62, 82 (1979).

    Google Scholar 

  33. Capinera, J. L. Handbook of Vegetable Pests. https://doi.org/10.1016/C2017-0-01577-X. (Elsevier, 2020).

  34. Overton, K. et al. Global crop impacts, yield losses and action thresholds for fall armyworm (Spodoptera frugiperda): a review. Crop Prot. 145, 105641 (2021).

    Google Scholar 

  35. Mendesil, E. et al. The invasive fall armyworm, Spodoptera frugiperda, in Africa and Asia: responding to the food security challenge, with priorities for integrated pest management research. J. Plant Dis. Prot. 130, 1175–1206 (2023).

    Google Scholar 

  36. Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS ONE 11, e0165632 (2016).

    Google Scholar 

  37. Cock, M. J. W., Beseh, P. K., Buddie, A. G., Cafá, G. & Crozier, J. Molecular methods to detect Spodoptera frugiperda in Ghana, and implications for monitoring the spread of invasive species in developing countries. Sci. Rep. 7, 4103 (2017).

    Google Scholar 

  38. Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), in the Maize Fields of Karnataka, India. Curr. Sci. 115, 621 (2018).

    Google Scholar 

  39. Deshmukh, S. S. et al. Natural enemies of Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), a recent invasive pest on maize in South India. Fla. Entomol. 102, 619 (2019).

    Google Scholar 

  40. EPPO. EPPO Global Database. EPPO Global Database https://gd.eppo.int/taxon/LAPHFR/distribution (2024).

  41. Nagoshi, R. N., Meagher, R. L. & Hay-Roe, M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecol. Evol. 2, 1458–1467 (2012).

    Google Scholar 

  42. Westbrook, J., Fleischer, S., Jairam, S., Meagher, R. & Nagoshi, R. Multigenerational migration of fall armyworm, a pest insect. Ecosphere 10, e02919 (2019).

    Google Scholar 

  43. Tessnow, A. E., Nagoshi, R. N., Meagher, R. L. & Fleischer, S. J. Revisiting fall armyworm population movement in the United States and Canada. Front. Insect Sci. 3, 1104793 (2023).

    Google Scholar 

  44. Johnson, S. J. Migration and the life history strategy of the fall armyworm, Spodoptera frugiperda in the western hemisphere. Int. J. Trop. Insect Sci. 8, 543–549 (1987).

    Google Scholar 

  45. Bessin, R. Fall armyworm moth numbers increasing. Ky. Pest News https://kentuckypestnews.wordpress.com/2021/07/27/fall-armyworm-moth-numbers-increasing/ (2021).

  46. Joseph, S. V. Outbreak of fall armyworm caterpillars in Georgia turfgrass. Turf Ornam. Pest Manag. https://site.caes.uga.edu/entomologyresearch/2021/08/outbreak-of-fall-armyworm-caterpillars-in-georgia-turfgrass/ (2021).

  47. Kesheimer, K., Held, D. & Cobb, P. Controlling fall armyworm on lawns and turf. AM Auburn Univ. ANR-0172. https://www.aces.edu/wp-content/uploads/2018/12/ANR-0172-Controlling-Fall-Armyworms-on-Lawns-Turf_032421L-G (2021).

  48. Kuhar, T., Pozo, A. D. & Taylor, S. Fall armyworm outbreak in Virginia – turf, sod, small grains, late sweet corn, sorghum, and other crops at risk. Va. Ag Pest Crop Advis. https://blogs.ext.vt.edu/ag-pest-advisory/fall-armyworm-outbreak-in-virginia-turf-sod-small-grains-late-sweet-corn-sorghum-and-other-crops-at-risk/ (2021).

  49. Thoms, A. & Lewis, D. Fall armyworms – the surprise of 2021. Hortic. Home Pest News. https://hortnews.extension.iastate.edu/2021/09/fall-armyworms-surprise-2021 (2021).

  50. Yang, F., Wang, Z. & Kerns, D. L. Resistance of Spodoptera frugiperda to Cry1, Cry2, and Vip3Aa Proteins in Bt Corn and Cotton in the Americas: implications for the rest of the world. J. Econ. Entomol. 115, 1752–1760 (2022).

    Google Scholar 

  51. Nagoshi, R. N. & Meagher, R. L. Review of fall armyworm (Lepidoptera: Noctuidae) genetic complexity and migration. Fla. Entomol. 91, 546–554 (2008).

    Google Scholar 

  52. Garriott, E. Forecasts and warnings. Mon. Weather Rev. 30, 553–554 (1902).

    Google Scholar 

  53. Young, F. Weather of North America and adjacent oceans. Mon. Wea. Rev. 49, 358–359 (1921).

    Google Scholar 

  54. Herring, J. Evidence for hurricane transport and dispersal of aquatic Hemiptera. Pan-Pac. Entomol. 34, 174–175 (1958).

    Google Scholar 

  55. Freeman, B. A fallout of black witches (Ascalapha odorata) associated with Hurricane Claudette. N. Lepidopterists’ Soc. 45, 71 (2003).

    Google Scholar 

  56. Salih, A. A. M., Baraibar, M., Mwangi, K. K. & Artan, G. Climate change and locust outbreak in East Africa. Nat. Clim. Chang. 10, 584–585 (2020).

    Google Scholar 

  57. Knutson, T. et al. Tropical cyclones and climate change assessment: Part I: detection and attribution. Bull. Am. Meteorol. Soc. 100, 1987–2007 (2019).

    Google Scholar 

  58. Knutson, T. et al. Tropical cyclones and climate change assessment: part ii: projected response to anthropogenic warming. Bull. Am. Meteorol. Soc. 101, E303–E322 (2020).

    Google Scholar 

  59. Chapman, J. W. et al. Wind selection and drift compensation optimize migratory pathways in a high-flying moth. Curr. Biol. 18, 514–518 (2008).

    Google Scholar 

  60. Reynolds, A. M., Reynolds, D. R., Sane, S. P., Hu, G. & Chapman, J. W. Orientation in high-flying migrant insects in relation to flows: mechanisms and strategies. Philos. Trans. R. Soc. B 371, 20150392 (2016).

    Google Scholar 

  61. Beuzelin, J. M., Larsen, D. J., Roldán, E. L. & Schwan Resende, E. Susceptibility to chlorantraniliprole in fall armyworm (Lepidoptera: Noctuidae) populations infesting sweet corn in Southern Florida. J. Econ. Entomol. 115, 224–232 (2022).

    Google Scholar 

  62. USDA. Adoption of Genetically Engineered Crops in the United States – Recent Trends in GE Adoption. USDA Economic Research Service https://www.ers.usda.gov/data-products/adoption-of-genetically-engineered-crops-in-the-united-states/recent-trends-in-ge-adoption#:~:text=Domestic%20Bt%20corn%20acreage%20grew,to%2037%20percent%20in%202001. (2025).

  63. Tabashnik, B. E. & Carrière, Y. Surge in insect resistance to transgenic crops and prospects for sustainability. Nat. Biotechnol. 35, 926–935 (2017).

    Google Scholar 

  64. Caprio, M. A. Source-sink dynamics between transgenic and non-transgenic habitats and their role in the evolution of resistance. ec 94, 698–705 (2001).

    Google Scholar 

  65. Yanai, M. Formation of tropical cyclones. Rev. Geophys. 2, 367–414 (1964).

    Google Scholar 

  66. Riley, J. R., Reynolds, D. R. & Farmery, M. J. Observations of the flight behaviour of the army worm moth, Spodoptera exempta, at an emergence site using radar and infra-red optical techniques. Ecol. Entomol. 8, 395–418 (1983).

    Google Scholar 

  67. Odiyo, P. O. The use of biogeographical techniques in the study of migrant noctuid moths. Int. J. Trop. Insect Sci. 8, 551–559 (1987).

    Google Scholar 

  68. Rose, A. H., Silversides, R. H. & Lindquist, O. H. Migration flight by an aphid, Rhopalosiphum maidis (Hemiptera: Aphididae), and a noctuid, Spodoptera frugiperda (Lepidoptera: Noctuidae). Can. Entomol. 107, 567–576 (1975).

    Google Scholar 

  69. Pashley, D. P. Host-associated genetic differentiation in fall armyworm (Lepidoptera: Noctuidae): a sibling species complex?. Ann. Entomol. Soc. Am. 79, 898–904 (1986).

    Google Scholar 

  70. Pashley, D. P. Quantitative genetics, development, and physiological adaptation in host strains of fall armyworm. Evolution 42, 93–102 (1988).

    Google Scholar 

  71. Nagoshi, R. N. & Meagher, R. L. Behavior and distribution of the two fall armyworm host strains in florida. Fla. Entomol. 87, 440–449 (2004).

    Google Scholar 

  72. Calixto, E. S. & Paula-Moraes, S. V. Hydrogen stable isotopes indicate reverse migration of fall armyworm in North America. Insects 16, 471 (2025).

    Google Scholar 

  73. Westbrook, J. K., Nagoshi, R. N., Meagher, R. L., Fleischer, S. J. & Jairam, S. Modeling seasonal migration of fall armyworm moths. Int J. Biometeorol. 60, 255–267 (2016).

    Google Scholar 

  74. NASA POWER Project. NASA POWER (Prediction of Worldwide Energy Resources): Agroclimatology solar andmeteorological data [dataset]. Hampton (VA): NASA Langley Research Center (2024).

  75. NASA POWER. National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) PredictionOf Worldwide Energy Resources (POWER) Project funded through the NASA Earth Science/Applied Science Program.NASA (2025).

  76. Rienecker, M. M. et al. The GEOS-5 data assimilation system: Documentation of versions 5.0.1, 5.1.0, and 5.2.0.NASA/TM-2008-104606. Vol. 27. (Greenbelt (MD), NASA Goddard Space Flight Center 2008).

  77. Rienecker, M. M. et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).

    Google Scholar 

  78. Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. The GEOS‑5 Atmospheric General Circulation Model: Mean Climateand Development from MERRA to Fortuna. NASA/TM‑2012‑104606. Vol. 28 (2012).

  79. Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev. 8, 1339–1356 (2015).

    Google Scholar 

  80. Bosilovich, M. G., Robertson, F. R., Takacs, L., Molod, A. & Mocko, D. Atmospheric water balance and variability in the MERRA-2 reanalysis. J. Clim. 30, 1177–1196 (2017).

    Google Scholar 

  81. Westbrook, J. K. Noctuid migration in Texas within the nocturnal aeroecological boundary layer. Integr. Comp. Biol. 48, 99–106 (2008).

    Google Scholar 

  82. R Core Team. R: A language and environment for statistical computing. (2024).

  83. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).

    Google Scholar 

  84. Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.3.0. (2020).

  85. Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.4.7 (2022).

  86. Agostinelli, C. & Lund, U. R package ‘circular’: Circular Statistics. R package version 0.5-0 (2023).

  87. Morellato, L. P. C., Alberti, L. F. & Hudson, I. L. Applications of circular statistics in plant phenology: a case studies approach. in Phenological Research (eds Hudson, I. L. & Keatley, M. R.) 339–359. https://doi.org/10.1007/978-90-481-3335-2. (Springer Netherlands, 2010).

  88. Novaes, L. R. et al. Environmental variables drive phenological events of anemocoric plants and enhance diaspore dispersal potential: A new wind-based approach. Sci. Total. Environ. 730, 139039 (2020).

    Google Scholar 

  89. Calixto, E. S. et al. Climate seasonality drives ant–plant–herbivore interactions via plant phenology in an extrafloral nectary-bearing plant community. J. Ecol. 109, 639–651 (2021).

    Google Scholar 

  90. Fox, J. & Weisberg, S. An R Companion to Applied Regression. 3rd ed. (Thousand Oaks (CA), Sage 2019).

  91. Wassenaar, L. & Hobson, K. Comparative equilibration and online technique for determination of non-exchangeable hydrogen of keratins for use in animal migration studies. Isot. Environ. Health Stud. 39, 211–217 (2003).

    Google Scholar 

  92. Ma, C., Vander Zanden, H. B., Wunder, M. B. & Bowen, G. J. assignR: an R package for isotope-based geographic assignment. Methods Ecol. Evol. 11, 996–1001 (2020).

    Google Scholar 

  93. Courtiol, A. et al. Isoscape computation and inference of spatial origins with mixed models using the R package isorix. in Tracking animal migration with stable isotopes 207–236. https://doi.org/10.1016/B978-0-12-814723-8.00009-X. (Elsevier, 2019).

  94. Paula-Moraes, S. V. et al. Continental-scale migration patterns and origin of Helicoverpa zea (Lepidoptera: Noctuidae) based on a biogeochemical marker. Environ. Entomol. nvae034 https://doi.org/10.1093/ee/nvae034. (2024).

  95. Hobson, K. A., Wassenaar, L. I. & Taylor, O. R. Stable isotopes (δD and δ13C) are geographic indicators of natal origins of monarch butterflies in eastern North America. Oecologia 120, 397–404 (1999).

    Google Scholar 

  96. Hobson, K. A., Kardynal, K. J. & Koehler, G. Expanding the isotopic toolbox to track monarch butterfly (Danaus plexippus) origins and migration: on the utility of stable oxygen isotope (δ18O) measurements. Front. Ecol. Evol. 7, 224 (2019).

    Google Scholar 

  97. IAEA/WMO. Global network of isotopes in precipitation. The GNIP database https://nucleus.iaea.org/wiser (2023).

  98. Calixto, E. S. & Paula-Moraes, S. Data from: Tropical cyclones impact the dispersal of a globally invasive moth pest. 200506 Bytes figshare https://doi.org/10.6084/M9.FIGSHARE.31169626 (2026).

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Acknowledgements

We thank the members of the Entomology lab at the West Florida Research and Education Center, UF, for their assistance with insect collection and preparation, and Jason Curtis, from the Department of Geological Sciences, University of Florida, for conducting the hydrogen isotope analysis. This research was partially funded by Cotton Inc., and by National Institute of Food and Agriculture, Florida Hatch project and Multistate Group NC246 – Ecology and Management of Arthropods in Corn.

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Conceptualization: E.C. and S.P.-M. Methodology: E.C. and S.P.-M. Data collection: E.C. and S.P.-M. Formal analysis: E.C. Funding acquisition: S.P.-M. Supervision: S.P.-M. Visualization: E.C. Writing–original draft: E.C. Writing—review and editing: E.C. and S.P.-M.

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Silvana V. Paula-Moraes.

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Calixto, E.S., Paula-Moraes, S.V. Tropical cyclones impact the dispersal of a globally invasive moth pest.
Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03328-y

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