The Role of Remote Sensing Techniques in Predicting Sand Fly Distribution

1. The Role of Remote Sensing Techniques in Predicting Sand Fly Distribution MAJ Michelle Colacicco-Mayhugh, PhD Chief, Department of Sand Fly Biology Division of Entomology Walter Reed Army Institute of Research

2. Outline • Background • Example: Ecological niche model of Phlebotomuspapatasi • Outstanding issues: • Applicability of normalized difference vegetation index (NDVI) to sand fly modeling • Utility of remotely sensed soil data • Future applications

3. Background • Sand flies develop in the substrate • Relative humidity and temperature important in development • Precipitation, temperature, and soil moisture are important factors in habitat suitability • Good candidate for remote sensing based studies

4. Ecological Niche Model of Phlebotomuspapatasi MG Colacicco-Mayhugh, PM Masuoka, JP Grieco. (2010) Ecological niche model of Phlebotomusalexandri and P. papatasi(Diptera: Psychodidae) in the Middle East. International Journal of Health Geographics. 9:2.

5. Phlebotomus papatasi • Leishmania major (cutaneous leishmaniasis) • Sand Fly Fever Virus • Distribution: Mediterranean Basin Balkans Northern Africa Middle East Central Asia India

6. Materials and Methods • Species presence records: • Literature (1968-2007) • Operations Iraqi & Enduring Freedom (2003-06) • Turkey (2006) • Environmental layers • Worldclim (www.worldclim.org) • Bioclimatic variables • Elevation • Global Land Cover Classification • Earth Resources Observation & Science (EROS) Data Center, U.S. Geological Survey • 96 land cover classes, 60 in study area

8. Model Building • Maxent version 3.2.1 http://www.cs.princeton.edu/~schapire/maxent/ • Training Points: 75% of points used to build the model • Test Points: 25% used to validate the model

9. Phlebotomus papatasi

10. Model evaluation • Area under the curve • Pseudo-absence: developed by using background data, which is chosen at random from the study area • Best possible AUC is something less than 1 • Training AUC = 0.944 • Test AUC = 0.884

11. Model evaluation • Minimum training presence • Threshold = lowest probability of presence associated with a training point • χ2 test to determine if model predicts the prob. of presence of test points sign. better than random P. papatasi, p < 0.0001

12. Variable Contribution • Jackknife procedure • One variable at a time excluded • Only one variable included • All variables included

13. Summary • Land cover • Urban land cover class associated with high probability for both species • Bare desert related to low probability of presence for P. papatasi • Bioclimatic variables • Contributed to model development, but were not important in isolation

14. Applicability of NDVI

15. Why examine NDVI? • Success using NDVI in modeling certain disease systems (i.e., Rift Valley Fever) • Studies using NDVI: • Bavia et al (2005): relationship between Lutzomyia spp. and NDVI • Gebre-Michael et al (2004): • NDVI in models of P. martini and P. orientalis • NDVI not among the best ecological determinants for either species • Cross et al. (1996): P. papatasidistribution in Southwest Asia on NDVI and temperature • Early niche modeling attempts using NDVI not successful

16. Probability of occurrence of P. papatasibased on AVHRR NDVI data. Red indicates higher probability, green indicates lower probability. (Cross et al. 1996)

17. Sand Fly Collections • P. papatasicollection records from Apr - Sep 2005 • Collection records separated by month • Trap nights calculated for each unique sampling location

18. NDVI Max filter 250-m resolution Mean filter

19. Summary • May be some relationship between early and late season collections and winter NDVI • Indicate of overwintering site quality • Success of diapausing flies • Need to further explore the relationship between sand flies and NDVI

20. Remotely Sensed Soil Data • Link between soil moisture and/or composition and sand fly abundance (Gebre-Michael et al 2004; Wasserberg et al 2002) • Remote sensing soil data is in process of development/refinement • ERS scatterometer (Institute of Photogrammetry and Remote Sensing, Vienna Institute of Technology) • NASA, Soil Moisture Active & Passive (SMAP)

21. Future Work • Develop models of medically important species • Expanding/refining models of P. alexandriand P. papatasi • Developing models of P. sergenti, and P. perfiliewi • Identify key links between sand fly abundance and remotely sensed data that may be used as predictors • NDVI • Soil moisture

22. Future Work • Move from basic understanding of the key factors to developing models that can predict population spikes, range expansion, etc. • Develop methods to provide real-time data upload/download to deployed entomologists

23. Questions? Disclaimer: Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. Images by Judy Stoffer, WRBU