Investigation of Spatial Mosquito Population Trends Using EOF Analysis: Model Vs Count Data in Pasco County Florida Cory Morin

Presentation Outline  Outline of Objectives of Study  Background of Research – Why Study Mosquitoes?  Introduction to DyMSiM  Model Runs + Correlation and Regression Coefficients  EOF Analysis  Conclusions and Discussion

Objectives  Validate Model (DyMSiM) with Mosquito Count Data –Using 25 Locations within Pasco County Florida ( , ) –Correlation Coefficients (Daily) –Regression Coefficients (Daily, Weekly, and Monthly) –EOF Analysis of Model and Trap Data  Spring, Summer, and Fall (weekly)

Mosquitoes: Aedes Aegypti  Characteristics –Urban, Container Breeding Mosquito –Tropical Habitat –Dengue Fever Vector  Dengue Fever –100 Million Cases a Year Worldwide –4 Serotypes without Cross Immunity –Dengue Hemorrhagic Fever from Multiple Infections Picture taken from general.info/IMG/Aedes-Aegypti-2.jpg Picture from dengue/map-distribution-2005.htm

Mosquitoes: Culex Quinquefasciatus Image taken from InfectiousDiseases/WestNileVirus.asp  Characteristics –Urban Mosquito –Feeds on Humans and Animals –West Nile Virus Vector  West Nile Virus –Arrived in New York 1999 –Symptoms: Mild Fever-Encephalitis Data from CDC.gov

Modeling Mosquitoes  Inputs –Temperature, Precipitation, Latitude –Evaporation Derived (Hamon’s Equation) –Irrigation/Land Cover  Governing Rules –Development Rates –Death Rates –Reproductive Rates –Larval/Pupa Capacity –Water Flux (sources and sinks)

Conceptual Model (DyMSiM) Dynamic Mosquito Simulation Model

Data  Temperature Data was Obtained from the National Climate Data Center  Precipitation Data was Obtained from the National Climate Data Center and The Pasco County Vector and Mosquito Control District  Mosquito Data was Obtained from the Pasco County Vector and Mosquito Control District Image from

Sample of Model Run

Regression + Correlation Coefficients  Regression Coefficient –Best fit line in the data that minimizes the sum of the square of the error –Shows how the magnitude of one variable changes with another  Correlation Coefficient –Calculated from the square root of the variance explained –Describes the relationship between two variables (Range from -1 to 1)

Correlation/Pearson Coefficients Time SpanD-Value Average Correlation Significant (0.95) Yes Yes Time Span Daily Regression Weekly Regression Monthly Regression

EOF Analysis  Used to Analyze Spatial Patterns in a Dataset  The 1st EOF Shows the Largest Fraction of Variance Explained in a Dataset –Found from Eigenvalues and Eigenvectors –Only a limited number of EOFs are Significant (North Test)

Spring North Test - The first two EOFs in both Whisker Plots are Significant

EOF 1 for Spring 1 st EOF for Trap Data 1 st EOF for Model Data

EOF 2 for Spring 2 nd EOF Trap Data 2 nd EOF Model Data

Summer North Test Only EOF 1 is Significant for the Summer

EOF 1 for Summer 1 st EOF for Trap Data 1 st EOF for Model Data

Fall North Test The 1 st and 2 nd EOFs are Significant

EOF 1 for Fall 1 st EOF for Trap Data 1 st EOF for Model Data

EOF 2 for Fall 2nd EOF for Trap Data 2 nd EOF Model Data

Conclusions  1 st EOF Dominates in Each Season for both Trap and Model Data –One individual location sticks out in particular (Large Population)  2 nd EOF: Model and Trap Data share some common characteristics but are not identical  Physical Mechanisms Behind the EOFs Need to be Analyzed (Surface Cover / Precipitation Patters)  Overall, the EOF Analysis Supports the Utility and Accuracy of DyMSiM

Model Limitations  “All Models are Wrong, Some are Useful” -George Box  The model only accounts for climate and land use factors –Predation, Pesticides, Food Availability, Human Behaviors, and Migration are not accounted for  Trap Data is Not Truth –Trapping mosquitoes may largely effect population dynamics –Microenvironments are important for mosquitoes but are not caught with climate data

Thank You for Your Attention Any Questions?