Spatial Modeling of Mosquito Densities Using MODIS Enhanced Vegetation Index (EVI) and Near Ground Humidity Indexes: adult female Culex tarsalis and Aedes vexans clustering in Colorado and Louisiana A PRESENTATION TO THE SUMMER COLLOQUIUM ON CLIMATE AND HEALTH JULY 23, 2004, NCAR, BOULDER COLORADO RUSSELL BARBOUR PH.D. VECTOR ECOLOGY LABORATORY YALE SCHOOL OF MEDICINE NEW HAVEN CT.

MODELING VERSUS INTERPOLATION LINEAR MODELING ATTEMPTS TO IDENTIFY FACTORS THAT INFLUENCE THE PARAMETERS OF INTEREST AND EXPLAIN OBSERVED VARIATION SPATIAL MODELING OR INTERPOLATION USE THE MATHEMATICAL PROPERTIES OF THE DATA ITSELF TO ESTIMATE VALUES AT UNKNOWN LOCATIONS

LECTURE OUTLINE Review basic concepts of spatial auto-correlation Demonstrate application of these methods to estimate mosquito vectors of West Nile Virus

BASIC CONCEPTS OF SPATIAL AUTO-CORRELATION Tobler’s first law of geography: Everything is related to everything else, but near things are more related than distant things Auto- Correlation violates the assumption of independence in that is made in most statistical tests Ordinary Least Squares Regression (OLS) for example, will tend to Type I Error ( falsely find significant relationships) if auto-correlation is present Auto-Correlation can be used to estimate values at un-sampled locations

QUANTIFYING AUTO-CORRELATION Moran’s I Geary’s C ratio Anselin’s Local Index of Spatial Autocorrelation (LISA) 0R Local Moran’s I

Moran’s I Similar to Pearson’s correlation coefficient, values between –1.0 and + 1. Index for dispersion/random/cluster patterns Indices close to zero, indicate random pattern Indices above zero indicate a tendency toward clustering Indices below zero indicate a tendency toward dispersion/uniform Most commonly reported indicator of spatial auto-correlation Differences from correlation coefficient are: one variable only, not two variables Incorporates weights (wij) which index “distance” between the locations

MORAN’S I CONTINUED GLOBAL MORANS’ I Estimates the level of aggregation of values or clustering in space for all observations Correlogram Morans’I calculated for observations grouped into specific distances

TYPES OF SPATIAL STRUCTURE DETECTED BY POSITIVE MORANS’I VALUES CLUSTERS: DATA FOUND IN CLOSE PROXIMITY TRENDS: A GRADIENT USUALLY CAUSED BY A GEOGRAPHIC FEATURE (NON- STATIONARITY) AUTO-CORRELATION: SIMILARITY OF OBSERVATIONS CLOSE TO EACH OTHER. A CLUSTER MAY OR MAY NOT HAVE AUTO- CORRELATION

STATIONARITY IN SPACE FIRST ORDER (STRICT) STATIONARITY A property of a spatial process where all of the spatial random variables have the same mean and variance value. INTRINSIC (WEAK) STATIONARITY An assumption that the data comes from a random process with a constant mean, and a semivariogram that only depends on the distance and direction separating any two locations. SOURCE : U. OF ARIZONA

PURPOSE OF LOUISIANA SPATIAL MOSQUITO ESTIMATES INDICATE AREAS OF HUMAN RISK OF WEST NILE VIRUS ASSIST DECISION MAKERS FOR VECTOR CONTROL INTERVENTIONS ASSESS THE EFFECTIVENESS OF CONTROL MEASURES ESTIMATES HAVE NO EXPLANATORY VALUE, STRICTLY A PROCESS OF CAPTURING MATHEMATICAL RELATIONSHIPS

Aedes vexans FLOOD WATER MOSQUITO STRONG FLIER > 24 Km/ DAY SOURCE : SERVICE 1976 FLOOD WATER MOSQUITO STRONG FLIER > 24 Km/ DAY AGGRESSIVE HUMAN BITER LOW INFECTION RATES HIGH TRANSMISSION EFFICIENCY IF SYSTEMICALLY INFECTED (TURELL 2001)

Aedes vexans NJ Light Trap Catches 2003 St Tammany MONTH NUMBER % OF TOTAL JUNE 30312 65.01% MAY 8144 24.69% MARCH 1565 APRIL 6243 21.52%

NJ LIGHT TRAP CATCHES JUNE 2003 GLOBAL MORANS’ I Aedes vexans NJ LIGHT TRAP CATCHES JUNE 2003 ST. TAMMANY PARISH LA Spatial Autocorrelation for Point Data: ---------------------------------------   Sample size 53   Moran's "I" 0.090325 Spatially random (expected) "I" -0.019231 Standard deviation of "I" 0.040462 Normality significance (Z) 2.707580 = P < .05 Randomization significance (Z) 2.952694 = P < .05

GLOBAL MORANS I VALUES Aedes vexans Catches NJ Light Traps 2003 MONTH OBS I EXP I P FEB -0.02 -.019 0.9656 MARCH 0.037 -0.019 0.1402 APRIL 0.081 0.0087 MAY 0.167 .0000 JUNE 0.090 0.0031

CORRELOGRAM FOR JUNE NJ LIGHT TRAP CATCHES ALL SPECIES APRIL 2003 ST TAMMANY PARISH LA. RANGE meters

ISOTROPIC VARIOGRAM Aedes vexans APRIL 2003 SAMPLE VARIANCE

MODIS ATMOSPHERE PRODUCTS 1-KM SPATIAL RESOLUTION USING THE NEAR-INFRARED ALGORITHM DURING THE DAY, 1-KM PIXEL RESOLUTION THE SOLAR RETRIEVAL ALGORITHM RELIES ON OBSERVATIONS OF WATER-VAPOR ATTENUATION OF REFLECTED SOLAR RADIATION IN THE NEAR-INFRARED IN THE ATMOSPHERE CLOSE TO THE GROUND VALUES REPRESENT THE AMOUNT OF WATER PER PIXEL THAT COULD THEORETICALLY BE PRECIPITATED OUT OF THE ATMOSPHERE

MODIS ATMOSPHERE PRODUCT RELATIONSHIP TO Aedes vexans WATER COLUMN PRODUCTS BY NIR AND IR ARE APPROXIMATIONS OF ABSOLUTE HUMIDITY AND SATURATION DEFICIT IN THE LOWER ATMOSPHERE THE IR DATA IS PRODUCED FOR BOTH DAY AND NIGHT.. INCLUDES DUSK AND DAWN ESTIMATES IS AVAILABLE ON A DAILY BASES

MEASUREMENT OF Aedes vexans MICRO-CLIMATE DISPERSAL PARAMETERS MODIS WATER VAPOR PRODUCTS RAINFALL STANDING WATER HIGH ABSOLUTE HUMIDITY HATCHING >EMERGENCE > DISPERSAL LIGHT TRAP DATA AND VARIOGRAPHY CLUSTERING NEAR HOSTS MORANS’I

RAINFALL ON Ae. vexans CLUSTERING CLUSTERING ON MODIS VALUES P VALUE .54 CLUSTERING ON MODIS VALUES P VALUE .104

SPATIAL FACTORS ASSOCIATED WITH Aedes vexans DENSITY IN St TAMMANY PARISH MODIS WATER VAPOR PRODUCT .38 URBAN PRESENCE (POPULATION AND LIGHT) .34

MODIS INFRARED WATER VAPOR COLUMN MONTHLY DATA 2003 TEMPERATURES ABOVE 90 F

Aedes vexans CATCHES in NJ LIGHT TRAPS VERSUS MODIS HUMIDITY MEASURES 2003 START OF HIGH TEMPERATURE

LAKE PONCHATRAIN

ESTIMATED DENSITY OF Aedes vexans ADULTS BY CO-KRIGING LIGHT TRAP AND MODIS HUMIDITY DATA

LIGHT TRAP DATA WITH WEAKER SPATIAL STRUCTURE Culex tarsalis IS INCRIMINATED IN THE TRANSMISSION OF WEST NILE VIRUS TO HUMANS IN THE FORT COLLINS COLORADO AREA (NASCI ET AL 2003) BREEDS IN ANY SOURCE OF FRESH WATER OTHER THAN TREE HOLES . MULTIPLE GENERATIONS IRRIGATION DITCHES HIGHLY FAVORABLE BREEDING AREAS FEEDS ON BIRDS THEN SHIFTS TO MAMMALS AND HUMANS AS ABUNDANCE INCREASES

Culex tarsalis

Culex tarsalis DISPERSAL From (Reisen 2002) SLOW MOVING, 1 Km/ DAY WIND DRIVEN ACTIVITY RELATED TO VEGETATION COVER

Culex tarsalis Clustering near Ft Collins Col. July 2003 Spatial Autocorrelation for Point Data: ---------------------------------------   Sample size 70 Moran's "I" -0.010883 Spatially random (expected) “I” -0.014493 Standard deviation of "I“ 0.020686 Normality significance (Z) 0.174495 =p> .10 Randomization significance (Z) 0.179128 =p> .10

PRESENCE OF IRRIGATED FARM LAND .26 EVI .12 WATER VAPOR DATA .34 SPATIAL RELATIONSHIPS OF C. tarsalis LIGHT TRAP DATA AND REMOTELY SENSED MICROCLIMATE INDICATORS MODIS REMOTELY SENSED VEGETATION INDEX = ENHANCED VEGETATION INDEX (EVI) MODIS WATER VAPOR COLUMN IR DATA INDICATORS SPATIAL RELATIONSHIP PRESENCE OF IRRIGATED FARM LAND .26 EVI .12 WATER VAPOR DATA .34

APPLICATION OF ARTIFICIAL NEURAL NETWORKING (ANN) TO IMPROVE ESTIMATES IRRIGATION, VEGETATION AND WATER VAPOR INDICATORS COMBINED BY ARTIFICIAL NEURAL NETWORKING (ANN) RESPONSE SURFACE ANN RESPONSE SURFACE = .61 SPATIAL RELATIONSHIP WITH C. tarsalis LIGHT TRAP CATCHES

MODIS ENHANCED VEGETATION INDEX (EVI) 2003 MAY 9 JUNE 10 JULY 2

CLUSTERING OF C. tarsalis in RELATIONSHIP TO MODIS EVI VALUES SPATIAL ASSOCIATION WITH EVI VALUES OF 4000- 6000 DURING MOST OF THE 2003 SEASON

RESULTS AN ASSOCIATION APPEARS TO EXIST BETWEEN C. tarsalis AND MODIS EVI AND WATER VAPOR VALUES AT THE COLORADO SITE A STRONGER ASSOCIATION BETWEEN Aedes vexans AND MODIS HUMIDITY DATA WAS FOUND AT THE LOUISIANA SITE EVEN WEAKLY CLUSTERING SPECIES CAN BE ESTIMATED THROUGH APPLICATION OF SPATIAL STATISTICS AND ARTIFICIAL NEURAL NETWORKS MORE ROBUST INTERPRETATION OF LIGHT TRAP DATA IS POSSIBLE DAILY MODIS ATMOSPHERIC DATA AVAILABILITY WILL ALLOW FORWARD LOOKING MODELS IN THE NEAR FUTURE