Safety in Numbers: Cost-effective Endangered Species Management for Viable Populations.

We develop a bioeconomic model to identify the cost-effective control of an invasive species (rainbow trout) to achieve a population viability goal for an endangered species (humpback chub) in the Grand Canyon of the U.S. Southwest. Solving the population viability problem is difficult since avoidin...

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Publicado en:Land Economics Vol. 95; no. 3; pp. 435 - 454
Autores principales: Donovan, Pierce, Bair, Lucas S., Yackulic, Charles B., Springborn, Michael R.
Formato: Artículo
Publicado: University of Wisconsin Press Aug2019
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Safety in Numbers: Cost-effective Endangered Species Management for Viable Populations.
      aug:
        au:
          Donovan, Pierce
          Bair, Lucas S.
          Yackulic, Charles B.
          Springborn, Michael R.
        affil:
          Ph.D. student, Department of Agricultural and Resource Economics, University of California, Davis
          Economist, Grand Canyon Monitoring and Research Center, Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, Arizona
          Research statistician, Grand Canyon Monitoring and Research Center, Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, Arizona
          Associate professor, Department of Environmental Science and Policy, University of California, Davis
      su:
        Grand Canyon (Ariz.)
        Endangered species
        Population
        Rainbow trout
        Dynamic programming
        Introduced species
      sug:
        subj:
          Endangered species
          Population
          Grand Canyon (Ariz.)
          Rainbow trout
          Dynamic programming
          Introduced species
      ab: We develop a bioeconomic model to identify the cost-effective control of an invasive species (rainbow trout) to achieve a population viability goal for an endangered species (humpback chub) in the Grand Canyon of the U.S. Southwest. Solving the population viability problem is difficult since avoiding a threshold with a given confidence imposes a probabilistic restriction on joint outcomes (survival) over many periods. We develop a novel dynamic programming solution approach that is fast and forgoes the simulation method requirement of imposing structure on the policy function. We also investigate an adaptive management model that incorporates learning about uncertain biological dynamics. (JEL Q22, Q57)
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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