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129 Figure 4.17. Risk map for RVF virus transmission to humans in Virginia. The risk ranges from 0.50 – 8.50, with a mean risk of 4.62. 129
CHAPTER 5. DISCUSSION 5.1 ENVIRONMENTAL-CLIMATIC VARIABLES AS PREDICTORS OF MOSQUITO DENSITY Pearson’s Product Moment Correlation Coefficient analyses addressed mosquito density spatial and temporal patterns through the use of remote sensing data. Significant correlations between mosquito and environmental-climatic data affirmed that predicting the spatial and temporal dynamics of disease vector species is feasible. Although there were variations in climate-population relationships among the regions, Normalized Difference Vegetation Index (NDVI), elevation, temperature, and precipitation all appear to be important predictors of mosquito abundance and distribution in Virginia. Overall, it was affirmed that mosquito populations can be influenced by variations in climate, as measured by Normalized Difference Vegetation Index (NDVI). NDVI is a proxy variable of local conditions (vegetation and water) that affect the development of mosquitoes. Although, mosquito abundance and distribution in Virginia is directly correlated with NDVI, the relationship varied among regions. In Northern Virginia, mosquito populations tend to increase with increasing NDVI. Conversely, in the Hampton Roads region mosquito population density was greater where NDVI values were lower. The inverse relationship between mosquito population and NDVI in the Hampton Roads region may partly be a function of land-cover. Most of the 130
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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
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Table 3.4. Worldclim interpolated c
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3.2.5 DATA PROCESSING All data sets
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normalize differences among surveil
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for three regions of Virginia - Nor
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Correlation analyses The Pearson‟
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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 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: 128 The weighted suitability modeli
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,
Page 183 and 184: Glass GE, Cheek JE, Patz JA, Shield
Page 185 and 186: Komar O, Robbins MB, Klenk K, Blitv
Page 187 and 188: NASA Earth Observatory (EO) Measuri
Page 189 and 190: Phillips SJ, Dudík M, Schapire RE.
Page 191 and 192: Tatem AJ, Hay SI, Rogers DJ. Global
Page 193 and 194: WHO Disease Outbreak News [Internet
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