Cory Morin Presentation Outline Outline of Objectives of Study


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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 (1995-1997,2002-2004)
    • 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



Mosquitoes: Culex Quinquefasciatus



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



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



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



EOF 1 for Spring



EOF 2 for Spring



Summer North Test



EOF 1 for Summer



Fall North Test



EOF 1 for Fall



EOF 2 for Fall



Conclusions

  • 1st EOF Dominates in Each Season for both Trap and Model Data

    • One individual location sticks out in particular (Large Population)
  • 2nd 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

  • Thank You for Your Attention

  • Any Questions?




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