Many high-risk plant pathogens are transported over long distances (hundreds of meters to thousands of kilometers) in the atmosphere. The ability to track the movement of these pathogens in the atmosphere is essential for forecasting disease spread and establishing effective quarantine measures. Here, we discuss the scales of atmospheric dispersal of plant pathogens along a transport continuum (pathogen scale, farm scale, regional scale, and continental scale). Growers can use risk information at each of these dispersal scales to assist in making plant disease management decisions, such as the timely application of appropriate pesticides. Regional- and continental-scale atmospheric features known as Lagrangian coherent structures (LCSs) may shuffle plant pathogens along highways in the sky. A promising new method relying on overlapping turbulent back-trajectories of pathogen-laden parcels of air may assist in localizing potential inoculum sources, informing local and/or regional management efforts such as conservation tillage. The emergence of unmanned aircraft systems (UASs, or drones) to sample plant pathogens in the lower atmosphere, coupled with source localization efforts, could aid in mitigating the spread of high-risk plant pathogens.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Andersen AL. 1.  1948. The development of Gibberella zeae headblight of wheat. Phytopathology 38:595–611 [Google Scholar]
  2. Asai GN. 2.  1960. Intra- and inter-regional movement of urediospores of black stem rust in the upper Mississippi Valley. Phytopathology 50:535–46 [Google Scholar]
  3. Aylor DE. 3.  1986. A framework for examining the inter-regional aerial transport of fungal spores. Agric. For. Meteorol. 38:263–88 [Google Scholar]
  4. Aylor DE. 4.  1999. Biophysical scaling and passive dispersal of fungal spores: relationship to integrated pest management strategies. Agric. For. Meteorol. 97:275–92 [Google Scholar]
  5. Aylor DE. 5.  2003. Spread of plant disease on a continental scale: role of aerial dispersal of pathogens. Ecology 84:1989–97 [Google Scholar]
  6. Aylor DE. 6.  1998. The aerobiology of apple scab. Plant Dis. 82:838–49 [Google Scholar]
  7. Aylor DE, Boehm MT, Shields EJ. 7.  2006. Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modeling. J. Appl. Meteorol. 45:1003–15 [Google Scholar]
  8. Aylor DE, Fry WE, Mayton H, Andrade-Piedra JL. 8.  2001. Quantifying the rate of release and escape of Phytophthora infestans sporangia from a potato canopy. Phytopathology 91:1189–96 [Google Scholar]
  9. Aylor DE, Schmale DG, Shields EJ, Newcomb M, Nappo CJ. 9.  2011. Tracking the potato late blight pathogen in the atmosphere using unmanned aerial vehicles and Lagrangian modeling. Agric. For. Meteorol. 151:251–260 [Google Scholar]
  10. Aylor DE, Sutton TB. 10.  1992. Release of Venturia inaequalis ascospores during unsteady rain: relationship to spore transport and deposition. Phytopathology 82:532–40 [Google Scholar]
  11. Aylor DE, Taylor GS, Raynor GS. 11.  1982. Long-range transport of tobacco blue mold spores. Agric. Meteorol. 27:217–32 [Google Scholar]
  12. Bennett RS, Milgroom MG, Sainudiin R, Cunfer BM, Bergstrom GC. 12.  2007. Relative contribution of seed-transmitted inoculum to foliar populations of Phaeosphaeria nodorum. Phytopathology 97:584–91 [Google Scholar]
  13. Boehm MT, Aylor DE. 13.  2005. Lagrangian stochastic modeling of heavy particle transport in the convective boundary layer. Atmos. Environ. 39:4841–50 [Google Scholar]
  14. Bowman KP, Pan LL, Campos T, Gao R. 14.  2007. Observations of fine-scale transport structure in the upper troposphere from the High-Performance Instrumented Airborne Platform for Environmental Research. J. Geophys. Res. 112:D18111 [Google Scholar]
  15. BozorgMagham AE, Ross SD. 15.  2015. Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast uncertainty. Commun. Nonlinear Sci. Numer. Simul. 22:964–979 [Google Scholar]
  16. BozorgMagham AE, Ross SD, Schmale DG. 16.  2013. Real-time prediction of atmospheric Lagrangian coherent structures based on uncertain forecast data: an application and error analysis. Phys. D Nonlinear Phenom. 258:47–60 [Google Scholar]
  17. Brown JKM, Hovmøller MS. 17.  2002. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297:537–41 [Google Scholar]
  18. Burleigh JR, Kramer CL, Collins TI. 18.  1967. A spore sampler for use in aircraft. Phytopathology 57:434–36 [Google Scholar]
  19. David R, BozorgMagham A, Schmale D, Ross S, Marr L. 19.  2014. Correlation and causality analyses of meteorological variables to examine predictors of Fusarium graminearum ascospore release. Proc. Natl. Fusarium Head Blight Forum, 2014, St. Louis Dec. 7–9. http://scabusa.org/pdfs/forum14_proc_complete.pdf [Google Scholar]
  20. Danielsen EF. 20.  1961. Trajectories: isobaric, isentropic and actual. J. Atmos. Sci. 18:479–86 [Google Scholar]
  21. Davis JM. 21.  1987. Modeling the long-range transport of plant pathogens in the atmosphere. Annu. Rev. Phytopathol. 25:169–88 [Google Scholar]
  22. Dellnitz M, Junge O, Koon WS, Lekien F, Lo MW. 22.  et al. 2005. Transport in dynamical astronomy and multibody problems. Int. J. Bifurc. Chaos 15:699–727 [Google Scholar]
  23. Dellnitz M, Junge O, Lo MW, Marsden JE, Padberg K. 23.  et al. 2005. Transport of Mars-crossing asteroids from the quasi-Hilda region. Phys. Rev. Lett. 94:231102 [Google Scholar]
  24. Dill-Macky R, Jones RK. 24.  2000. The effect of previous crop residues and tillage on Fusarium head blight of wheat. Plant Dis. 84:71–76 [Google Scholar]
  25. Draxler RR, Hess GD. 25.  1998. An overview of the HYSPLIT_4 modeling system for trajectories, dispersion, and deposition. Aust. Meteorol. Mag. 47:295–308 [Google Scholar]
  26. Draxler RR, Rolph GD. 26.  2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model. Silver Spring, MD: NOAA Air Resour. Lab http://www.arl.noaa.gov/ready/hysplit4.html [Google Scholar]
  27. Elkinton JS, Carde RT, Mason CJ. 27.  1984. Of time-average dispersion models for estimating pheromone concentration in a deciduous forest. J. Chem. Ecol. 10:1081–108 [Google Scholar]
  28. Emanuel PA, Buckley PE, Sutton TA, Edmonds JM, Bailey AM. 28.  et al. 2012. Detection and tracking of a novel genetically tagged biological simulant in the environment. Appl. Environ. Microbiol. 78:8281–88 [Google Scholar]
  29. Farrell JA, Pang S, Li W. 29.  2003. Plume mapping via hidden Markov methods. IEEE Trans. Syst. Man Cybern. 33:850–63 [Google Scholar]
  30. Fay B, Glaab H, Jacobsen I, Schrodin R. 30.  1995. Evaluation of Eulerian and Lagrangian atmospheric transport models at the Deutscher Wetterdienst using ANATEX surface tracer data. Atmos. Environ. 29:2485–97 [Google Scholar]
  31. Fernando WGD, Miller JD, Seaman WL, Seifert K, Paulitz TA. 31.  2000. Daily and seasonal dynamics of airborne spores of Fusarium graminearum and other Fusarium species sampled over wheat plots. Can. J. Bot. 78:497–505 [Google Scholar]
  32. Flesch TK, Wilson JD, Yee E. 32.  1995. Backward-time Lagrangian stochastic dispersion models and their application to estimate gaseous emissions. J. Appl. Meteorol. 34:1320–32 [Google Scholar]
  33. Fulton JD. 33.  1966. Microorganisms of the upper atmosphere: V. Relationship between frontal activity and the micropopulation at altitude. Appl. Microbiol. 14:245–50 [Google Scholar]
  34. Gottwald TR, Tedders WL. 34.  1985. A spore trap for use on aerial remotely piloted vehicles. Phytopathology 75:801–7 [Google Scholar]
  35. Gregory PH. 35.  1973. The Microbiology of the Atmosphere New York: Wiley & Sons [Google Scholar]
  36. Griffin DW. 36.  2007. Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clin. Microbiol. Rev. 20:459 [Google Scholar]
  37. Guo XW, Fernando WGD, Bullock P, Sapirstein H. 37.  2010. Quantifying cropping practices in relation to inoculum levels of Fusarium graminearum on crop stubble. Plant Pathol. 59:1107–13 [Google Scholar]
  38. Guo S, Yang R, Zhang H, Weng W, Fan W. 38.  2009. Source identification for unsteady atmospheric dispersion of hazardous materials using Markov Chain Monte Carlo method. Int. J. Heat Mass Transf. 52:3955–62 [Google Scholar]
  39. Haller G. 39.  2002. Lagrangian coherent structures from approximate velocity data. Phys. Fluids A 14:1851–61 [Google Scholar]
  40. Haller G, Yuan G. 40.  2000. Lagrangian coherent structures and mixing in two-dimensional turbulence. Phys. D Nonlinear Phenom. 147:352–70 [Google Scholar]
  41. Harrison CS, Glatzmaier GA. 41.  2011. Lagrangian coherent structures in the California current system: sensitivities and limitations. Geophys. Astrophys. Fluid Dyn. 106:22–44 [Google Scholar]
  42. Hastings A, Cuddington K, Davies KF, Dugaw CJ, Elmendorf S. 42.  et al. 2005. The spatial spread of invasions: new developments in theory and evidence. Ecol. Lett. 8:91–101 [Google Scholar]
  43. Hernandez-Ceballos M, Adame J, Bolívar J, De la Morena B. 43.  2013. Vertical behaviour and meteorological properties of air masses in the south-west of the Iberian Peninsula (1997–2007). Meteorol. Atmos. Phys. 119:163–75 [Google Scholar]
  44. Hernandez-Ceballos M, García-Mozo H, Adame J, Domínguez-Vilches E, Bolívar J. 44.  et al. 2011. Determination of potential sources of Quercus airborne pollen in Cordoba city (southern Spain) using back-trajectory analysis. Aerobiologia 27:261–76 [Google Scholar]
  45. Isard SA, Gage SH. 45.  2001. Flow of Life in the Atmosphere East Lansing, MI: Mich. State Univ. Press [Google Scholar]
  46. Isard SA, Gage SH, Comtois P, Russo JM. 46.  2005. Principles of the atmospheric pathway for invasive species applied to soybean rust. Bioscience 55:10851–60 [Google Scholar]
  47. Keller MD, Thomason WE, Schmale DG. 47.  2011. The spread of a released clone of Gibberella zeae from different amounts of infested corn residue. Plant Dis. 95:1458–64 [Google Scholar]
  48. Keller MD, Waxman KD, Bergstrom GC, Schmale DG. 48.  2010. Local distance of wheat spike infection by released clones of Gibberella zeae disseminated from infested corn residue. Plant Dis. 94:1151–55 [Google Scholar]
  49. Kennedy R, Wakeham AJ, Byrne KG, Meyer UM, Dewey FM. 49.  2000. A new method to monitor airborne inoculum of the fungal plant pathogens Mycosphaerella brassicicola and Botrytis cinerea. Appl. Environ. Microbiol. 66:2996–3003 [Google Scholar]
  50. Kot M, Lewis MA, van den Driessche P. 50.  1996. Dispersal data and the spread of invading organisms. Ecology 77:2027–42 [Google Scholar]
  51. Legg B, Raupach M. 51.  1982. Markov-chain simulation of particle dispersion in inhomogeneous flows: the mean-drift velocity induced by a gradient in Eulerian velocity variance. Bound. Layer Meteorol. 24:3–13 [Google Scholar]
  52. Lekien F, Ross SD. 52.  2010. The computation of finite-time Lyapunov exponents on unstructured meshes and for non-Euclidean manifolds. Chaos 20:017505 [Google Scholar]
  53. Lermusiaux PFJ. 53.  2006. Uncertainty estimation and prediction for interdisciplinary ocean dynamics, special issue on “uncertainty quantification.”. J. Comput. Phys. 217:176–99 [Google Scholar]
  54. Lin B, BozorgMagham A, Ross SD, Schmale DG. 54.  2013. Small fluctuations in the recovery of fusaria across consecutive sampling intervals with unmanned aircraft 100 m above ground level. Aerobiologia 29:45–54 [Google Scholar]
  55. Lin B, Ross SD, Prussin AJ, Schmale DG. 55.  2014. Seasonal associations and atmospheric transport distances of fungi in the genus Fusarium collected with unmanned aerial vehicles and ground-based sampling devices. Atmos. Environ. 94:385–91 [Google Scholar]
  56. Luschi P, Hays GC, Del Seppia C, Marsh R, Papi F. 56.  1998. The navigational feats of green sea turtles migrating from Ascension Island investigated by satellite telemetry. Proc. Biol. Sci. R. Soc. 265:2279–84 [Google Scholar]
  57. Maldonado-Ramirez SL, Schmale DG, Shields EJ, Bergstrom GC. 57.  2005. The relative abundance of viable spores of Gibberella zeae in the planetary boundary layer suggests the role of long-distance transport in regional epidemics of Fusarium head blight. Agric. For. Meteorol. 132:20–27 [Google Scholar]
  58. Marburger DA, Venkateshwaran M, Conley SP, Esker PD, Lauer JG, Ané JM. 58.  2015. Crop rotation and management effect on Fusarium spp. Populations. Crop. Sci. 55:365–76 [Google Scholar]
  59. Mitarai S, Siegel DA, Watson JR, Dong C, McWilliams JC. 59.  2009. Quantifying connectivity in the coastal ocean with application to the Southern California Bight. J. Geophys. Res. 114:C10026 [Google Scholar]
  60. Morris CE, Monteil CL, Berge O. 60.  2013. The life history of Pseudomonas syringae: linking agriculture to Earth system processes. Annu. Rev. Phytopathol. 51:85–104 [Google Scholar]
  61. Myrick AJ, Baker TC. 61.  2011. Locating a compact odor source using a four-channel insect electroantennogram sensor. Bioinspir. Biomim. 6:016002 [Google Scholar]
  62. 62. North Am. Plant Dis. Forecast Cent 2007. Cucurbit downy mildew. Raleigh, NC: NCSU http://www.ces.ncsu.edu/depts/pp/cucurbit/ [Google Scholar]
  63. 63. North Am. Plant Dis. Forecast Cent 2007. Soybean rust. Raleigh, NC: NCSU http://www.ces.ncsu.edu/depts/pp/soybeanrust/ [Google Scholar]
  64. 64. North Am. Plant Dis. Forecast Cent 2007. Tobacco blue mold. Raleigh, NC: NCSU http://www.ces.ncsu.edu/depts/pp/bluemold/ [Google Scholar]
  65. Ojiambo PS, Holmes GJ. 65.  2011. Spatiotemporal spread of cucurbit downy mildew in the eastern United States. Phytopathology 101:451–61 [Google Scholar]
  66. Oke TR. 66.  1987. Boundary Layer Climates. Cambridge, UK: Cambridge Univ. Press, 2nd ed.. [Google Scholar]
  67. Okubo A, Levin SA. 67.  2001. Diffusion and Ecological Problems: Modern Perspectives 14 Dordrecht, Neth: Springer [Google Scholar]
  68. Oyekan J, Hu H. 68.  2010. A novel bio-controller for localizing pollution sources in a medium Peclet environment. J. Bionic Eng. 7:345–53 [Google Scholar]
  69. Paulitz TC. 69.  1996. Diurnal release of ascospores by Gibberella zeae in inoculated wheat plots. Plant Dis. 80:674–78 [Google Scholar]
  70. Periyannan S, Moore J, Ayliffe M, Bansal U, Wang X. 70.  et al. 2013. The gene Sr33, an ortholog of barley Mla genes, encodes resistance to wheat stem rust race Ug99. Science 341:786–88 [Google Scholar]
  71. Prakash NU, Vasantharaj R, Balasubramanian E, Bhushan G, Das S, Eqbal F. 71.  2014. Design, development and analysis of air mycoflora using a fixed wing unmanned aerial vehicle (UAV). J. Appl. Sci. Eng. 17:1–8 [Google Scholar]
  72. Prussin AJ, Li Q, Malla R, Ross SD, Schmale DG. 72.  2014. Monitoring the long distance transport of Fusarium graminearum from field-scale sources of inoculum. Plant Dis. 98:504–11 [Google Scholar]
  73. Prussin AJ, Marr LC, Schmale DG, Stoll R, Ross SD. 73.  2015. Experimental validation of a long-distance transport model for plant pathogens: application to Fusarium graminearum. Agric. For. Meteorol. 203:118–30 [Google Scholar]
  74. Prussin AJ, Szanyi NA, Welling PI, Ross SD, Schmale DG. 74.  2014. Estimating the production and release of ascospores from a field-scale source of Fusarium graminearum inoculum. Plant Dis. 98:497–503 [Google Scholar]
  75. Raben SG, Ross SD, Vlachos PP. 75.  2014. Computation of finite-time Lyapunov exponents from time-resolved particle image velocimetry data. Exp. Fluids 55:1638 [Google Scholar]
  76. Raben SG, Ross SD, Vlachos PP. 76.  2014. Experimental determination of three-dimensional finite-time Lyapunov exponents in multi-component flows. Exp. Fluids 55:1824 [Google Scholar]
  77. Roberts MJ, Schimmelpfennig D, Ashley E, Livingston M, Ash M, Vasavada U. 77.  2006. The value of plant disease early warning systems: a case study of USDA's soybean rust coordinated framework Econ. Res. Rep. Number 18, USDA Economic Res. Serv., Washington, DC [Google Scholar]
  78. Rolph GD. 78.  2003. Real-time Environmental Applications and Display sYstem (READY). Silver Spring, MD: NOAA Air Resourc. Lab http://ready.arl.noaa.gov/index.php [Google Scholar]
  79. Ross SD, Tallapragada P. 79.  2012. Detecting and exploiting chaotic transport in mechanical systems. Applications of Chaos and Nonlinear Dynamics in Science and Engineering 2 S Banerjee, L Rondoni, M Mitra 155–83 Dordrecht, Neth.: Springer [Google Scholar]
  80. Rypina II, Pratt LJ, Lozier MS. 80.  2011. Near-surface transport pathways in the North Atlantic ocean. J. Phys. Oceanogr. 41:911–25 [Google Scholar]
  81. Sadys M, Skjøth C, Kennedy R. 81.  2014. Back-trajectories show export of airborne fungal spores (Ganoderma sp.) from forests to agricultural and urban areas in England. Atmos. Environ. 84:88–99 [Google Scholar]
  82. Schmale DG, Arntsen QA, Bergstrom GC. 82.  2005. The forcible discharge distance of ascospores of Gibberella zeae. Can. J. Plant Pathol. 27:376–82 [Google Scholar]
  83. Schmale DG, Bergstrom GC. 83.  2003. Fusarium head blight. Plant Health Instr. doi:10.1094/PHI-I-2003-0612-01 [Google Scholar]
  84. Schmale DG, Bergstrom GC. 84.  2004. Spore deposition of the ear rot pathogen, Gibberella zeae, inside corn canopies. Can. J. Plant Pathol. 26:591–95 [Google Scholar]
  85. Schmale DG, Dingus BR, Reinholtz CF. 85.  2008. Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields. J. Field Robot. 25:133–47 [Google Scholar]
  86. Schmale DG, Leslie JF, Saleh AA, Shields EJ, Bergstrom GC. 86.  2006. Genetic structure of atmospheric populations of Gibberella zeae. Phytopathology 95:1021–26 [Google Scholar]
  87. Schmale DG, Ross SD, Fetters TL, Tallapragada P, Wood-Jones AK, Dingus B. 87.  2012. Isolates of Fusarium graminearum collected 40–320 meters above ground level cause Fusarium head blight in wheat and produce trichothecene mycotoxins. Aerobiologia 28:1–11 [Google Scholar]
  88. Schmale DG, Shields EJ, Bergstrom GC. 88.  2006. Night-time spore deposition of the Fusarium head blight pathogen, Gibberella zeae. Can. J. Plant Pathol. 28:100–8 [Google Scholar]
  89. Schmale DG, Wood-Jones AK, Cowger C, Bergstrom GC, Arellano C. 89.  2011. Trichothecene genotypes of Gibberella zeae from winter wheat fields in the eastern United States. Plant Pathol. 60:909–17 [Google Scholar]
  90. Schneider RW, Hollier CA, Whitam HK. 90.  2005. First report of soybean rust caused by Phakopsora pachyrhizi in the continental United States. Plant Dis. 89:774 [Google Scholar]
  91. Senatore C, Ross SD. 91.  2011. Detection and characterization of transport barriers in complex flows via ridge extraction of the finite time Lyapunov exponent field. Int. J. Numer. Methods Eng. 86:1163–74 [Google Scholar]
  92. Shadden SC, Lekien F, Marsden JE. 92.  2005. Definition and properties of Lagrangian coherent structures: mixing and transport in two-dimensional aperiodic flows. Phys. D 212:271–304 [Google Scholar]
  93. Shadden SC, Lekien F, Paduan JD, Chavez FP, Marsden JE. 93.  2009. The correlation between surface drifters and coherent structures based on high-frequency radar in Monterey Bay. Deep Sea Res. Part II Top. Stud. Oceanogr. 56:161–72 [Google Scholar]
  94. Singh RP, Hodson DP, Huerta-Espino J, Jin Y, Bhavani S. 94.  et al. 2011. The emergence of Ug99 races of the stem rust fungus is a threat to world wheat production. Annu. Rev. Phytopathol. 49:465–81 [Google Scholar]
  95. Stohl A. 95.  1998. Computation, accuracy and applications of trajectories: a review and bibliography. Atmos. Environ. 32:947–66 [Google Scholar]
  96. Stohl A, Wotawa G, Seibert P, Kromp-Kolb H. 96.  1995. Interpolation errors in wind fields as a function of spatial and temporal resolution and their impact on different types of kinematic trajectories. J. Appl. Meteorol. 342149–65 [Google Scholar]
  97. Tallapragada P, Ross SD. 97.  2013. A set oriented definition of finite-time Lyapunov exponents and coherent sets. Commun. Nonlinear Sci. Numer. Simul. 18:1106–26 [Google Scholar]
  98. Tallapragada P, Ross SD, Schmale DG. 98.  2011. Lagrangian coherent structures are associated with fluctuations in airborne microbial populations. Chaos 21:033122 [Google Scholar]
  99. Taylor G. 99.  1921. Diffusion by continuous movements. Proc. Lond. Math. Soc. 20:196–202 [Google Scholar]
  100. Techy L, Schmale DG, Woolsey CA. 100.  2010. Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous unmanned aerial vehicles. J. Field Robot. 27:335–43 [Google Scholar]
  101. Techy L, Woolsey CA, Schmale DG. 101.  2009. Monitoring the spread of a plant pathogen in the lower atmosphere using unmanned aerial vehicles and a buoyancy-controlled weather balloon. SAE Tech. Pap. doi:10.4271/2009-01-3125 [Google Scholar]
  102. Tschanz A, Horst K, Nelson PE. 102.  1975. Ecological aspects of ascospore discharge in Gibberella zeae. Phytopathology 65:597–99 [Google Scholar]
  103. Trail F, Xu H, Loranger R, Gadoury D. 103.  2002. Physiological and environmental aspects of ascospore discharge in Gibberella zeae (anamorph Fusarium graminearum). Mycologia 94:181–89 [Google Scholar]
  104. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T. 104.  et al. 1995. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res. 23:4407–14 [Google Scholar]
  105. Wiggins S. 105.  1988. Stirred but not mixed. Nature 333:395–96 [Google Scholar]
  106. Yee E, Lien FS, Keats A, D'Amours R. 106.  2008. Bayesian inversion of concentration data: source reconstruction in the adjoint representation of atmospheric diffusion. J. Wind Eng. Ind. Aerodyn. 96:1805–16 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error