Advertisement

Aerobiologia

, Volume 29, Issue 1, pp 45–54 | Cite as

Small fluctuations in the recovery of fusaria across consecutive sampling intervals with unmanned aircraft 100 m above ground level

  • Binbin Lin
  • Amir Bozorgmagham
  • Shane D. Ross
  • David G. Schmale III
Original Paper

Abstract

The aerobiology of fungi in the genus Fusarium is poorly understood. Recent work has highlighted the role of Lagrangian coherent structures (LCSs) in the movement of fusaria in the lower atmosphere. Here, we extend this work by examining the relationship between the length of atmospheric sampling intervals with autonomous unmanned aerial vehicles (UAVs) and the recovery of fusaria. UAVs were equipped with an array of eight microbe-sampling devices with four “inner” sampling arms and four “outer” sampling arms. Each set of arms was used to collect consecutive aerobiological samples during 10 min sampling periods at 100 m above ground level at the Kentland Farm in Blacksburg, Virginia. Fifty-one flights (102 consecutive sampling intervals) were conducted in 2010 and 2011. A correlation analysis showed that the counts of fusaria did not vary between the inner and outer sampling arms from consecutive sampling period of 10 min (r = 0.93, P < 0.001), and the frequency of colony counts had similar distributions for samples from the inner and outer sampling arms. An analysis of the temporal variation in the collections of Fusarium showed that the similarity between collections decreased over time. This work supports the idea that atmospheric populations of fusaria are well mixed, and large changes in the recovery of fusaria in the lower atmosphere may be attributed to large-scale phenomena (e.g., LCSs) operating across varying temporal and spatial scales. This work may contribute to effective control measures for diseases causes by fusaria in the future.

Keywords

Fungi Aerobiological sampling Pathogen Unmanned aerial vehicles UAV Lagrangian coherent structure Long-distance transport Atmospheric transport barrier Selective medium 

Notes

Acknowledgments

We thank John Cianchetti for his excellent technical assistance with the construction, maintenance, and operation of the UAVs described in this work. We also thank Zolton Bair for his help with fungal cultures and Pavlos Vlachos for helpful discussions. This material is based upon work supported by the National Science Foundation under Grant Numbers DEB-0919088 (Atmospheric transport barriers and the biological invasion of toxigenic fungi in the genus Fusarium) and CMMI-1100263 (Dynamical mechanisms influencing the population structure of airborne pathogens: Theory and observations). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References

  1. Aylor, D. E. (2003). Spread of plant disease on a continental scale: Role of aerial dispersal of pathogens. Ecology, 84, 1989–1997.CrossRefGoogle Scholar
  2. Berek, L., Petri, I. B., Mesterházy, Á., Téren, J., & Molnár, J. (2001). Effects of mycotoxins on human immune functions in vitro. Toxicology in Vitro, 15, 25–30.CrossRefGoogle Scholar
  3. Csanady, G. T. (1973). Turbulent diffusion in the environment. Dordrecht: D. Reidel Publishing Company.CrossRefGoogle Scholar
  4. Dosio, A., Vila-Guerau De Arellano, J., Holtslag, A. M., & Builtjes, P. J. H. (2005). Relating Eulerian and Lagrangian statistics for the turbulent dispersion in the atmospheric convective boundary layer. Journal of the Atmospheric Sciences, 62, 1175–1191.CrossRefGoogle Scholar
  5. Gifford, F. A. (1987). The time-scale of atmospheric diffusion considered in relation to the universal diffusion function f1. Atmospheric Environment, 21, 1315–1320.Google Scholar
  6. Lekien, F., & Ross, S. D. (2010). The computation of finite-time Lyapunov exponents on unstructured meshes and for non-Euclidean manifolds. Chaos, 20, 017505.CrossRefGoogle Scholar
  7. Leslie, J. F., & Summerell, B. A. (2006). The Fusarium laboratory manual. Ames, Iowa: Blackwell Publishing.Google Scholar
  8. McMullen, M., Jones, R., & Gallenberg, D. (1997). Scab of wheat and barley: A re-emerging disease of devastating impact. Plant Disease, 81, 1340–1348.CrossRefGoogle Scholar
  9. Okubo, A., & Levin, S. A. (2001). Diffusion and ecological problems: modern perspectives. New York: Springer.Google Scholar
  10. Schmale, D. G., Dingus, B. R., & Reinholtz, C. (2008). Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields. Journal of Field Robotics, 25, 133–147.CrossRefGoogle Scholar
  11. Schmale, D. G., Leslie, J. F., Zeller, K. A., Saleh, A. A., Shields, E. J., & Bergstrom, G. C. (2006). Genetic structure of atmospheric populations of Gibberella zeae. Phytopathology, 96, 1021–1026.CrossRefGoogle Scholar
  12. Schmale, D., Ross, S., Fetters, T., Tallapragada, P., Wood-Jones, A., & Dingus, B. (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.CrossRefGoogle Scholar
  13. Senatore, C., & Ross, S. D. (2011). Detection and characterization of transport barriers in complex flows via ridge extraction of the finite time Lyapunov exponent field. International Journal for Numerical Methods in Engineering, 86, 1163–1174.CrossRefGoogle Scholar
  14. Tallapragada, P., Ross, S. D., & Schmale, D. G. (2011). Lagrangian coherent structures are associated with fluctuations in airborne microbial populations. Chaos, 21, 033122.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Binbin Lin
    • 1
  • Amir Bozorgmagham
    • 2
  • Shane D. Ross
    • 2
  • David G. Schmale III
    • 1
  1. 1.Department of Plant Pathology, Physiology, and Weed ScienceVirginia TechBlacksburgUSA
  2. 2.Department of Engineering Science and MechanicsVirginia TechBlacksburgUSA

Personalised recommendations