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For the Hampton Roads region, extracting values by trap for April-October 2005- 2006 mosquito surveillance data, the number of data points for which associations were measured was 2,525. Statistical analyses revealed significant positive correlations between the monthly mean competent vector population and monthly mean precipitation, long-term mean monthly minimum temperature, annual mean temperature (bio 01), temperature seasonality (bio 04), mean temperature of the wettest quarter (bio 08), mean temperature of the driest quarter (bio 09), mean temperature of the warmest quarter (bio 10), mean temperature of the coldest quarter (bio 11), and precipitation seasonality (bio 15). There is a negative correlation between the average monthly competent vector population and elevation, monthly mean maximum NDVI, long-term mean monthly maximum NDVI, mean diurnal range (bio 02), isothermality (bio 03), maximum temperature of the warmest month (bio 05), temperature annual range (bio 07), annual precipitation (bio 12), precipitation of the wettest month (bio 13), precipitation of the driest month (bio 14), precipitation of the wettest quarter (bio 16), precipitation of the driest quarter (bio 17), precipitation of the warmest quarter (bio 18), and precipitation of the coldest quarter (bio 19). Precipitation is correlated with mean maximum temperature (r = -0.135, p < 0.05). NDVI is correlated with elevation (r = 0.316, p < 0.05) and precipitation (r = -0.0396, p = < 0.05). For variable pairs with P values greater than 0.050 there is no significant relationship between the two variables. In summary, in the Hampton Roads region there is a significant inverse correlation between mosquito collection data and both NDVI and elevation data. Mosquito populations tend to decrease with both increasing NDVI and elevation. Converse to the Northern Virginia and Richmond-Petersburg regions, NDVI values were lower at lower elevations. There is a direct relationship between mosquito collection data and precipitation 117
data. Mosquito populations tend to increase with increasing precipitation. There is no significant correlation between mosquito collection data and temperature data, however, there is a significant positive correlation between bioclimatic variables that captured the affects of both temperature and precipitation. NDVI is greater at higher elevations and when precipitation is lower. Mean maximum temperature is lower when precipitation is greater. 118
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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
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mountain range of the Appalachian M
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The land area of Hampton Roads incl
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52 Table 3.1. Primary data sets and
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Table 3.2. Sources of Virginia mosq
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Figure 3.3. Cattle Density in Virgi
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Figure 3.6. 2006 Deer Abundance Est
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Land-Cover Global land-cover data a
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Figure 3.8. Virginia Land-cover Nor
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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
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: temperature of the coldest month (b
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
Page 165 and 166: 150 Figure A.5. Ae. triseriatus dis
Page 167 and 168: 152 Figure A.7. Cx. pipiens distrib
Page 169 and 170: 154 Figure A.9. Cx. salinarius dist
Page 171 and 172: Desiccation - drying. Diapause - ph
Page 173 and 174: Larva - immature form of certain or
Page 175 and 176: Vector competence - the vector comp
Page 177 and 178: REFERENCES Ackerman GA, Giroux J. A
Page 179 and 180: Brown H, Duik-Wasser M, Andreadis T
Page 181 and 182: 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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