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119 Table 4.9. Correlation between mosquito density and environmental-climatic predictor variables. The Pearson correlation coefficient (R) and P-value (in parentheses) are shown. Predictor Variable Northern Virginia (n = 1443) Richmond-Petersburg (n = 773) Hampton Roads (n = 2715) Correlation Coefficient (P-value) Correlation Coefficient (P-value) Correlation Coefficient (P-value) Elevation ***-0.0473 (0.0722) **-0.1030 (0.0043) -0.1070 (0.0000) Monthly mean maximum temperature Monthly mean minimum temperature Monthly mean precipitation Monthly mean maximum NDVI Long-term mean monthly maximum temperature Long-term mean monthly minimum temperature Long-term mean monthly mean precipitation Long-term mean monthly maximum NDVI *0.0851 (0.0012) -0.0692 (0.0544) 0.0302 (0.1290) 0.0842 (0.0014) -0.0573 (0.1120) 0.1760 (0.0000) -0.0237 (0.3690) -0.0290 (0.4210) 0.0479 (0.0161) 0.1410 (0.0000) -0.0682 (0.0581) -0.1900 (0.0000) 0.0873 (0.0009) -0.0953 (0.0080) 0.0273 (0.1700) 0.0865 (0.0010) -0.0923 (0.0103) 0.0692 (0.0005) 0.0464 (0.0781) -0.0516 (0.1520) -0.0353 (0.0761) 0.1320 (0.0000) -0.1850 (0.0000) -0.2120 (0.0000)
120 Predictor Variable Northern Virginia (n = 1443) Richmond-Petersburg (n = 773) Hampton Roads (n = 2715) Correlation Coefficient (P-value) Correlation Coefficient (P-value) Correlation Coefficient (P-value) Bio 01 0.0699 (0.0079) 0.1450 (0.0001) 0.0390 (0.0403) Bio 02 -0.0174 (0.5100) -0.0127 (0.7250) -0.2650 (0.0000) Bio 03 0.0432 (0.1010) 0.0570 (0.1130) -0.2740 (0.0000) Bio 04 -0.1550 (0.0000) -0.0237 (0.5100) 0.0741 (0.0002) Bio 05 -0.0207 (0.4320) 0.1380 (0.0001) -0.0787 (0.0000) Bio 06 0.0639 (0.0152) 0.1230 (0.0006) 0.1760 (0.0000) Bio 07 -0.0813 (0.00201 ) -0.0275 (0.4450) -0.1860 (0.0000) Bio 08 0.0092 (0.7280) -0.0737 (0.0404) 0.3110 (0.0000) Bio 09 0.0806 (0.0022) 0.0565 (0.1170) 0.0908 (0.0000) Bio 10 0.0378 (0.1510) 0.1320 (0.0002) 0.0428 (0.0315) Bio 11 0.0802 (0.0023) 0.1500 (0.0001) 0.0527 (0.0081) Bio 12 -0.0594 (0.0239) 0.0518 (0.1500) -0.1410 (0.0000) Bio 13 0.0445 (0.0911) 0.1020 (0.0047) -0.1190 (0.0000) Bio 14 0.0613 (0.0198) 0.0105 (0.7700) -0.2420 (0.0000) Bio 15 -0.0696 (0.0082) 0.1120 (0.0018) 0.0420 (0.0348) Bio 16 -0.0336 (0.2020) 0.1230 (0.0006) -0.1840 (0.0000) Bio 17 0.0510 (0.0528) 0.0658 (0.0676) -0.2800 (0.0000) Bio 18 -0.0492 (0.0617) 0.1280 (0.0003) -0.2480 (0.0000) Bio 19 0.0510 (0.0528) 0.0886 (0.0137) -0.3000 (0.0000) * The pair(s) of variables with positive correlation coefficients and P values below 0.050 (shown here in red) tend to increase together. ** For the pairs with negative correlation coefficients and P values below 0.050 (shown here in blue), one variable tends to decrease while the other increases. ***For pairs with P values greater than 0.050 (shown here in black), there is no significant relationship between the two variables.
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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
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 and 132: temperature of the coldest month (b
Page 133: data. Mosquito populations tend to
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,
Page 183 and 184: 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
Page 193 and 194:
WHO Disease Outbreak News [Internet
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