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Controlling Invasive Species in Controlling Invasive Species in an Urban- -Wildland Interface Wildland Interface an Urban George Frisvold George Frisvold Department of Agricultural & Resource Economics Department of Agricultural &


  1. Controlling Invasive Species in Controlling Invasive Species in an Urban- -Wildland Interface Wildland Interface an Urban George Frisvold George Frisvold Department of Agricultural & Resource Economics Department of Agricultural & Resource Economics University of Arizona University of Arizona October 22-23, 2009 Economic Research Service, USDA 1800 M Street NW Washington, DC

  2. Typical Sonoran Desert vegetation is poorly adapted to fire. Perennial buffelgrass forms dense stands, crowds out native vegetation, and readily carries fire.

  3. Spraying with Glyphosate the most Spraying with Glyphosate the most effective control method effective control method But, glyphosate only effective when plants have “greened up” after rainfall Rains uncertain & infrequent Timing and mobilizing labor is a major constraint Treatment is Leontief function of Labor, Chemicals, Equipment

  4. Conclusions First Conclusions First � Annual treatment budget determines damage path Annual treatment budget determines damage path � � Treatment start year does not affect trajectory of this path, Treatment start year does not affect trajectory of this path, � just how soon you get on it just how soon you get on it � “ “Rules of thumb Rules of thumb” ” used by land managers provide significant used by land managers provide significant � damage reductions damage reductions � Resource sharing not necessarily beneficial if agencies have Resource sharing not necessarily beneficial if agencies have � different objectives different objectives � Stakeholder response to results: Stakeholder response to results: � � Revisit local eradication as strategy Revisit local eradication as strategy � � Does possibilty of eradication change gains to cooperation? Does possibilty of eradication change gains to cooperation? �

  5. Objective Function: Minimize Objective Function: Minimize Damage Index subject to Damage Index subject to � Resource constraints (Budget & Labor) Resource constraints (Budget & Labor) � � Buffelgrass population dynamics equations Buffelgrass population dynamics equations � � Calibrated based on historical observations of Tumamoc Hill Calibrated based on historical observations of Tumamoc Hill � Desert Research Lab (DRL) Desert Research Lab (DRL) � Treatment (time) cost function Treatment (time) cost function � � Estimated via OLS based on DRL treatment data Estimated via OLS based on DRL treatment data � � Labor time the binding constraint Labor time the binding constraint � � Cost depends on plant density, distance from road, slope Cost depends on plant density, distance from road, slope �

  6. Tumamoc Hill & ‘ ‘A A’ ’ Mountain Tumamoc Hill & Mountain Simulation Site Simulation Site UA/USGS Desert Lab

  7. Damage Function Damage Function � Damage caused by buffelgrass in a cell depends on Damage caused by buffelgrass in a cell depends on � � Population density in cell Population density in cell � � Cells proximity to resources at risk (exponential decay) Cells proximity to resources at risk (exponential decay) � � D = D = λ λ S Saguaro + λ λ R Riparian + (1 – – λ λ S ) House S Saguaro + R Riparian + (1 S ) House � � Saguaro = risk to saguaros Saguaro = risk to saguaros � � Riparian = risk to riparian vegetation Riparian = risk to riparian vegetation � � House = fire risk to housing House = fire risk to housing �

  8. Buffelgrass population dynamics Buffelgrass population dynamics � Pre Pre- -treatment population at t depends on treatment population at t depends on � � Population at t Population at t – – 1 1 � � Population in surrounding cells at t Population in surrounding cells at t – – 1 1 � � Carrying capacity (K) Carrying capacity (K) � � Post Post- -treatment population treatment population � � Pre Pre- -treatment population x (1 treatment population x (1 – – k) k) � � k = 0.9 based on Desert Research Lab data k = 0.9 based on Desert Research Lab data � � Local eradication (population driven to 0) doesn Local eradication (population driven to 0) doesn’ ’t occur t occur � (we’ ’ll come back to this) ll come back to this) (we � 2,000 interrelated, non 2,000 interrelated, non- -linear state equations linear state equations � � This This is is rocket science! rocket science! �

  9. Control Strategies Control Strategies (given binding labor constraint) (given binding labor constraint) � Full dynamic optimization difficult Full dynamic optimization difficult � � Static optimization (rank based on D/C ratio) Static optimization (rank based on D/C ratio) � � Rules of thumb Rules of thumb � � “ “Treat twice Treat twice” ” give priority to acres treated in give priority to acres treated in � previous year for the first time previous year for the first time � Weight treatment priority based on carrying Weight treatment priority based on carrying � capacity, K capacity, K � Rules of thumb introduce dynamic considerations Rules of thumb introduce dynamic considerations � into static optimization into static optimization

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  11. Can heuristics & strategies be Can heuristics & strategies be developed by running simulations? developed by running simulations?

  12. Data Layers Data Layers � Cost function Cost function � � Plant Density Plant Density � Distance from Road � Distance from Road � � Slope Slope � � Resources at Risk Resources at Risk � � Riparian Vegetatin Riparian Vegetatin � � Houses Houses � � Saguaros Saguaros � � Others Possible Others Possible �

  13. Data Layers Data Layers � Carrying capacity, K Carrying capacity, K � � Aspect Aspect � � Soil Type Soil Type � � Disturbance Disturbance � � Altitude Altitude � � Damage Damage � � Population Density Population Density � � Proximity to Resources at Risk Proximity to Resources at Risk �

  14. Tumamoc / A Mountain as Test Site Tumamoc / A Mountain as Test Site � 2,000 acre site 2,000 acre site � � Multiple entities managing land Multiple entities managing land � � U of A, USGS, DOT, City Parks & Rec, Homeowners U of A, USGS, DOT, City Parks & Rec, Homeowners’ ’ � Association Association � Data layers are Excel worksheets Data layers are Excel worksheets � � Each acre on map represented by Excel cell Each acre on map represented by Excel cell � � Excel keeps track of spatial relationships Excel keeps track of spatial relationships � � Automatically generates maps Automatically generates maps �

  15. Disadvantages Disadvantages � Not full dynamic optimization Not full dynamic optimization � � Static optimization is a lower bound of Static optimization is a lower bound of � effectiveness effectiveness � Rules of thumb improve results Rules of thumb improve results � � Don Don’ ’t know how far we are from optimum t know how far we are from optimum �

  16. Advantages Advantages � People can input spatial data into Excel People can input spatial data into Excel � � Excel Solver generates maps of where to spray Excel Solver generates maps of where to spray � � Alternative to using Solver Alternative to using Solver � � Damage / Cost ratio maps Damage / Cost ratio maps � � This just another linked spreadsheet This just another linked spreadsheet � � Using Using “ “Surface Surface” ” option in Charts can be used to option in Charts can be used to � create maps of priority areas for treatment create maps of priority areas for treatment � Recommendations easy to interpret Recommendations easy to interpret �

  17. Damage / Cost Ratio & Treatment Damage / Cost Ratio & Treatment (under labor time constraint) (under labor time constraint) D/C ratio obtained from simple Recommended treatment area spreadsheet formulas Based on Excel Solver 0-5 5-10 10-15 S1 S1 S6 S6 S11 S11 S16 S16 S21 S21 S26 S26 S31 S31 S36 S36 1 7 13 19 25 31 37 43 49 1 6 11 16 21 26 31 36 41 46

  18. Labor lowers damage trajectory Labor lowers damage trajectory

  19. Saguaro Damage as a Function of Saguaro Damage as a Function of Start Year Start Year

  20. Damage Converging to New, Lower Damage Converging to New, Lower Trajectory Trajectory

  21. Population rebounds because Population rebounds because k = 0.9 k = 0.9 Should we model possibility of local eradication?

  22. Housing Damage as a Function of Housing Damage as a Function of Start Year Start Year

  23. Riparian Vegetation Damage as a Riparian Vegetation Damage as a Function of Start Year Function of Start Year

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