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Bioeconomic Analysis of Biological Invasions: Impacts, Uncertainty, and Welfare Maximization

This study delves into the bioeconomic analysis of biological invasions, focusing on the cumulative impacts of invasive species on biodiversity loss, aesthetics, property values, and the economy. It discusses the costs and benefits of invasions, emphasizing the need to maximize societal welfare through effective management strategies that consider uncertainty and ecological impacts.

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Bioeconomic Analysis of Biological Invasions: Impacts, Uncertainty, and Welfare Maximization

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  1. Biological invasions and the Millennium Assessment: bioeconomic analysis & uncertainty

  2. 200 Cichlid Extinctions

  3. Unionid Extirpation

  4. Sea Lamprey Damage

  5. Cumulative number of invasions Year Ricciardi (in review)

  6. Relevance • Major cause of biodiversity loss • Physical and chemical changes • Industrial impacts • Aesthetics • Property values • Invasive species cost estimated $137 Billion US/yr (Pimentel et al. 1999)

  7. Beneficial Introduced Species

  8. Bioeconomics Ecology Objective Source Location of Species Costs Transport/Survival in Pathway Maximize Societal Welfare Establishment AbundanceSpread Benefits Impact

  9. Bioeconomics Ecology Economics Objective Source Location of Species Transport / Introductions Prevention Costs Transport/Survival in Pathway Control Maximize Societal Welfare Value Added Investment Establishment AbundanceSpread Production Benefits Non-market Values Impact

  10. 25 yr horizon 5 yr horizon Welfare Cost Cost Time Leung et al. 2002

  11. Bioeconomics • Explicitly incorporate interactions between society, management & ecology • Incorporate time scale, GDP, and biological responses • Integrate non-market components • Challenges • Sparse data • High uncertainty (ecology, economic valuation) • Timely, but appropriate responses

  12. Uncertainty3 invasions, 100 lakes examined: p=0.03? Pr=0.28 Pr=0.57 Pr=0.04

  13. Learning event Learning event Effect of New Research

  14. Research Directions • Flexible frameworks for decision making • Ecological forecasting with realistically limited data • Integration and evaluation of uncertainty and learning on decisions

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