Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

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densities of humans creates a landscape conducive to the transmission of mosquito-borne diseases. 3.2 DATA SETS The major data sets used in this dissertation include Virginia mosquito vector, human demographic, domestic animal, wildlife, elevation, land-cover, Normalized Difference Vegetation Index (NDVI), temperature, precipitation, bioclimatic, and boundary data. The data sets are grouped as vector, host, environmental-climatic, and administrative boundary (Table 3.1) and described below. 51
52 Table 3.1. Primary data sets and sources Data set Description Source Vector Virginia mosquito surveillance data See Table 3.2 Host Cattle, sheep, and goat density data USDA Animal and Plant Health Inspection Service (APHIS) Centers for Epidemiology and Animal Health (CEAH) (http://www.aphis.usda.gov/vs/ceah/) From USDA Agricultural Census data published by the National Agricultural Statistics Service (http://www.nass.usda.gov/Census_of_Agriculture) Deer population abundance estimates Virginia Department of Game and Inland Fisheries (http://www.dgif.virginia.gov/) Human demographics density data U.S. Census Bureau (http://www.census.gov/) Environmental SRTM digital elevation data (1 km) Worldclim (http://www.worldclim.org/) Land-cover data (1 km) Boston University Department of Geography Land Cover and Land Use Dynamics (http://www-modis.bu.edu/landcover/) AVHRR NDVI data (8km) NASA (http://www.gsfc.nasa.gov/) Climate Monthly minimum temperature (1 km) PRISM (http://www.prism.oregonstate.edu/) Monthly maximum temperature (1 km) PRISM (http://www.prism.oregonstate.edu/) Monthly mean precipitation (1 km) PRISM (http://www.prism.oregonstate.edu/) Average monthly minimum temperature (1 km) Worldclim (http://www.worldclim.org/) Average monthly maximum temperature (1 km) Worldclim (http://www.worldclim.org/) Average monthly mean precipitation (1 km) Worldclim (http://www.worldclim.org/) Administrative Boundary Bioclim (1 km) Worldclim (http://www.worldclim.org/) State and county level boundaries ESRI Data & Maps CD included with ArcGIS Desktop FIPS Codes U.S. Census Bureau (http://www.census.gov/)
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Using Remote Sensing, Ecological Ni
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DEDICATION This is dedicated to my
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Lastly, I would like to thank all o
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CHAPTER 4: RESULTS ................
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LIST OF FIGURES Figures Page 2.1 Kn
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AUC - Area Under the Curve LIST OF
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ABSTRACT USING REMOTE SENSING, ECOL
Page 15 and 16: Keywords Rift Valley fever, risk as
Page 17 and 18: systematically inventory and monito
Page 19 and 20: populations), human demographics an
Page 21 and 22: The geographic distribution of pote
Page 23 and 24: human and animal health, economic l
Page 25 and 26: 1.8 ORGANIZATION OF DISSERTATION Th
Page 27 and 28: (e.g., ether, chloroform), detergen
Page 29 and 30: (Imam et al. 1979, Davies and Marti
Page 31 and 32: have demonstrated that wild ruminan
Page 33 and 34: flooding of grassland depressions c
Page 35 and 36: and created mosquito breeding habit
Page 37 and 38: may transmit the RVF virus mechanic
Page 39 and 40: virus during inter-epizootic period
Page 41 and 42: grown by nearly 9% per year (Tatem
Page 43 and 44: animals during RVF outbreaks or col
Page 45 and 46: sufficient and continued contact be
Page 47 and 48: Environmental factors are directly
Page 49 and 50: animals could arrive in an uninfect
Page 51 and 52: GIS technology and RS data has been
Page 53 and 54: NDVI is calculated as a normalized
Page 55 and 56: In September 2006, the RVF forecast
Page 57 and 58: In the U.S. RVF is not present and
Page 59 and 60: changes in mosquito populations in
Page 61 and 62: hosts that are widely dispersed thr
Page 63 and 64: mountain range of the Appalachian M
Page 65: The land area of Hampton Roads incl
Page 69 and 70: Table 3.2. Sources of Virginia mosq
Page 71 and 72: Figure 3.3. Cattle Density in Virgi
Page 73 and 74: Figure 3.6. 2006 Deer Abundance Est
Page 75 and 76: Land-Cover Global land-cover data a
Page 77 and 78: Figure 3.8. Virginia Land-cover Nor
Page 79 and 80: Figure 3.9. June 2006 Normalized Di
Page 81 and 82: Table 3.4. Worldclim interpolated c
Page 83 and 84: 3.2.5 DATA PROCESSING All data sets
Page 85 and 86: normalize differences among surveil
Page 87 and 88: for three regions of Virginia - Nor
Page 89 and 90: Correlation analyses The Pearson‟
Page 91 and 92: of maximum entropy (i.e., that is m
Page 93 and 94: When test data are available, MaxEn
Page 95 and 96: Algebra tool in the ArcGIS Spatial
Page 97 and 98: CHAPTER 4. RESULTS 4.1 POTENTIAL RV
Page 99 and 100: 1. If a native U.S. vector can tran
Page 101 and 102: Bioecology of Species Selected for
Page 103 and 104: ditches, animal waste lagoons, and
Page 105 and 106: 90 Mosquito Species Typical Habitat
Page 107 and 108: The majority of mosquitoes were col
Page 109 and 110: Table 4.5. Virginia competent vecto
Page 115 and 116: 4.3 MOSQUITO DENSITY CORRELATIONS W
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102 Table 4.8. Mean and range of ea
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Regional graphical analyses In Figu
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Population Density (mean daily trap
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Competent vector species were plott
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Predictor correlation analyses Whil
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temperature of the coldest month (b
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data. Mosquito populations tend to
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120 Predictor Variable Northern Vir
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122 was 0.003. At this threshold, t
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124 The Jackknife test of variable
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126 4.5 RISK OF RVF ESTABLISHMENT A
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128 The weighted suitability modeli
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CHAPTER 5. DISCUSSION 5.1 ENVIRONME
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heavily on rainfall as do the predo
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5.2 ENVIRONMENTAL-CLIMATIC VARIABLE
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study; predicted suitable habitat w
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state. The high risk in this region
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The results of this study indicate
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It is recommended that species dive
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individually and over smaller time
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146 APPENDIX A: RVF COMPETENT VECTO
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148 Figure A.3. Ae. sollicitans dis
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150 Figure A.5. Ae. triseriatus dis
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152 Figure A.7. Cx. pipiens distrib
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154 Figure A.9. Cx. salinarius dist
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Desiccation - drying. Diapause - ph
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Larva - immature form of certain or
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Vector competence - the vector comp
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REFERENCES Ackerman GA, Giroux J. A
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Brown H, Duik-Wasser M, Andreadis T
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Diallo M, Nabeth P, Ba K, Sall AA,
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Glass GE, Cheek JE, Patz JA, Shield
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Komar O, Robbins MB, Klenk K, Blitv
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NASA Earth Observatory (EO) Measuri
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Phillips SJ, Dudík M, Schapire RE.
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Tatem AJ, Hay SI, Rogers DJ. Global
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WHO Disease Outbreak News [Internet
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