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

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Petersburg City 296 11600 27 461 25.16 21.35 2.39 County Records b # Mosquitoes # Trap Sites 99 # Trap Nights # Mosquitoes per Trap Night Mean # Mosquitoes Abundance Index c Portsmouth City 707 39820 29 2405 16.56 14.76 1.57 Prince Edward County 52 6259 5 58 107.91 25.50 10.23 Prince George County 1 1 1 1 1.00 7.00 0.09 Prince William County 490 38259 57 2168 17.65 17.39 1.67 Pulaski County 86 1510 5 187 8.07 15.35 0.77 Richmond City 997 50999 101 2750 18.55 20.13 1.76 Roanoke City 21 1748 8 21 83.24 28.48 7.89 Roanoke County 180 3417 39 268 12.75 19.02 1.21 Rockingham County 7 168 2 7 24.00 22.29 2.28 Spotsylvania County 34 448 8 34 13.18 18.12 1.25 Stafford County 25 109 9 25 4.36 14.20 0.41 Suffolk City 2517 100031 56 13050 7.67 12.41 0.73 Sussex County 8 57 2 8 7.13 18.625 0.68 Virginia Beach City 2360 51934 107 7183 7.23 14.43 0.69 Wise County 441 73631 143 577 127.61 41.27 12.10 York County 49 1643 7 288 5.70 12.92 0.54 a The number of mosquitoes is the number of females trapped. b A record is the monthly mean number of mosquitoes of a competent vector species trapped at a given site. c The abundance indices were calculated using the following equation: [(total number of female mosquitoes collected in the county) / (total number of female mosquitoes collected in Virginia] / [(number of trap nights in the county) / (total number of trap nights in Virginia)].
4.3 MOSQUITO DENSITY CORRELATIONS WITH ENVIRONMENTAL-CLIMATIC VARIABLES The results presented here were obtained from analyses of mosquito surveillance data collected between April and October for 2005-2006 at the Northern Virginia, Richmond- Petersburg, and Hampton Roads trap sites shown in Figure 4.3. Mosquito surveillance data from these traps were collapsed to create records representing the monthly average number of competent vectors collected per trap site. A total of 1,443 records were included in Northern Virginia analyses; 2,525 and 773 records were included in analyses of the Hampton Roads and Richmond-Petersburg regions, respectively. The 2005-2006 mean and range recorded for each environmental-climatic predictor variable used in regional correlation analyses are summarized in Table 4.8. 100
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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
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Keywords Rift Valley fever, risk as
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systematically inventory and monito
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populations), human demographics an
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The geographic distribution of pote
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human and animal health, economic l
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1.8 ORGANIZATION OF DISSERTATION Th
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(e.g., ether, chloroform), detergen
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(Imam et al. 1979, Davies and Marti
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have demonstrated that wild ruminan
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flooding of grassland depressions c
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and created mosquito breeding habit
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may transmit the RVF virus mechanic
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virus during inter-epizootic period
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grown by nearly 9% per year (Tatem
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animals during RVF outbreaks or col
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sufficient and continued contact be
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Environmental factors are directly
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animals could arrive in an uninfect
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GIS technology and RS data has been
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NDVI is calculated as a normalized
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In September 2006, the RVF forecast
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In the U.S. RVF is not present and
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changes in mosquito populations in
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hosts that are widely dispersed thr
Page 63 and 64: mountain range of the Appalachian M
Page 65 and 66: The land area of Hampton Roads incl
Page 67 and 68: 52 Table 3.1. Primary data sets and
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 111 and 112: Table 4.6. Virginia competent vecto
Page 113: Table 4.7. Virginia competent vecto
Page 117 and 118: 102 Table 4.8. Mean and range of ea
Page 119 and 120: Regional graphical analyses In Figu
Page 121 and 122: Population Density (mean daily trap
Page 125 and 126: Competent vector species were plott
Page 129 and 130: Predictor correlation analyses Whil
Page 131 and 132: temperature of the coldest month (b
Page 133 and 134: data. Mosquito populations tend to
Page 135 and 136: 120 Predictor Variable Northern Vir
Page 137 and 138: 122 was 0.003. At this threshold, t
Page 139 and 140: 124 The Jackknife test of variable
Page 141 and 142: 126 4.5 RISK OF RVF ESTABLISHMENT A
Page 143 and 144: 128 The weighted suitability modeli
Page 145 and 146: CHAPTER 5. DISCUSSION 5.1 ENVIRONME
Page 147 and 148: heavily on rainfall as do the predo
Page 149 and 150: 5.2 ENVIRONMENTAL-CLIMATIC VARIABLE
Page 151 and 152: study; predicted suitable habitat w
Page 153 and 154: state. The high risk in this region
Page 155 and 156: The results of this study indicate
Page 157 and 158: It is recommended that species dive
Page 159 and 160: individually and over smaller time
Page 161 and 162: 146 APPENDIX A: RVF COMPETENT VECTO
Page 163 and 164: 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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