Integrated Bioeconomic Modeling of Invasive Species Management David Finnoff Jason Shogren John Tschirhart University of Wyoming Chad Settle University of Tulsa Brian Leung McGill University David Lodge University of Notre Dame Michael Roberts ERS/USDA August 2004 ERS

Progress—working toward integrating specific modeling approaches into one general framework Application to leafy spurge

Phase I: Endogenous Risk with discounting and risk aversion,

Endogenous Risk Captures risk-benefit tradeoffs Stresses that management priorities depend crucially on: The tastes of the manager — over time and risk bearing The technology of risk reduction —prevention, control, and adaptation Managers with different preferences will likely make different choices on the mix of prevention and control.

Investigate how changes in a manager’s preferences over time and over risk affect the optimal strategy mix: 1.Explore comparative statics on how changing tastes affect the technology mix. 2. Implement the model to a specific application of managing zebra mussels in a lake.

Schematic of the Invasion Process t=0 t=1 N I N N t=2 t=3 I I IHIH IHIH IHIH IHIH ILIL ILIL ILIL ILIL p1p1 (1-p 1 ) p2p2 (1-p 2 ) p3p3 (1-p 3 ) q2q2 qH3qH3 qL3qL3 q3q3 (1-q 2 ) (1-q 3 ) (1-q L 3 ) (1-q H 3 )

Dynamic Endogenous Risk Stage 1: Stage 2:

Comparative Statics – Risk Aversion

Simulation Results 1

Simulation Results 2

Leafy Spurge Application

Conclusions Explored how changes in a manager’s preferences for time and risk-bearing influence optimal strategy mix Impacts are species-specific & rest on whether direct effects dominate the other through indirect effects less risk averse managers who are far sighted, invest more in prevention, less in control, and require less private adaptation

Reduced risk aversion on the part of the manager yields lower probabilities of invasion, lower invader populations, and increased welfare Risk aversion induces a manager to want to avoid risk— both from the invader and from the input used - go with the safer bet—control More exploration into the underlying preferences of managers may be worthwhile to better understand how such effects might influence invasives management

Phase II: General Equilibrium, Competition, & the Influence of Fundamental Resources

GEEM

Temperature

Predictions Plant E(p i )

Invasion 1 Biomass, Plant 1 Biomass, Plant 2 Biomass, Plant 3

Invasion 2 Biomass, Plant 4 Biomass, Plant 6 Biomass, Plant 5 (Invader)

Humans Biomass HarvestsHerbicide

Conclusions Theory of plant competition based in individual plant physiological parameters and maximizing behavior Theory starts prior to the population dynamics and builds on a behavioral basis Captures redundancy in the plant community Species with max expected valued of SS SEL parabola(s) are only non-redundant species If invading species is non-redundant – it will dominate Limitations oOnly addresses resource competition oOmits mutualism & only considers mature plants & lacks age structure

Phase III: Optimal Control Model

Optimal Control Determines Paths to Steady State under different scenarios, with: – no action by ranchers/farmers & land managers –action taken only by ranchers/farmers –action taken by both Accounts for the impact of actions taken by ranchers/farmers

Flexibility to account for first-best path and welfare losses under second-best paths Allows for economically viable and non-viable harvesting of invasive Includes benefits/costs between steady states instead of simply a comparison of steady states

Species Equations of Motion

Representative Rancher/Farmer

Land Manager as a Social Planner

Conclusions Illustrate how accounting for actions by ranchers/farmers and feedbacks affect predictions on species populations Show how the various paths to a steady state are altered by activity/inactivity of each party Explore optimal action by land managers given model assumptions

Remaining tasks Phase IV: Leafy spurge in Thunder Basin Grasslands Phase V: Implications Phase VI: “Supermodel” validation