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Watershed Planning within a Quantitative Scenario Analysis Framework
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Published on: July 24, 2016

On valuing information in adaptive-management models.

Alana L Moore1, Michael A McCarthy

  • 1School of Botany, University of Melbourne, Parkville, Victoria 3010, Australia. moa@unimelb.edu.au

Conservation Biology : the Journal of the Society for Conservation Biology
|February 9, 2010
PubMed
Summary
This summary is machine-generated.

Active adaptive management balances near-term suboptimality for future gains. This study reveals active and passive policies are often similar, emphasizing monitoring is best when option benefits and learning payoffs are high.

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Area of Science:

  • Ecological Management
  • Environmental Science
  • Decision Science

Background:

  • Adaptive management strategies may appear suboptimal short-term for long-term learning benefits.
  • Optimal active (learning-inclusive) and passive (current information) policies often show similarity, posing a management paradox.
  • The broad applicability of local study findings and management objectives incorporating estimate variance can influence learning emphasis.

Purpose of the Study:

  • To investigate why active and passive adaptive management policies are frequently similar.
  • To explore the influence of learning benefits, monitoring costs, and management objectives on adaptive management strategies.
  • To analyze a case study on revegetation options at Merri Creek, Melbourne, Australia.

Main Methods:

  • Comparative analysis of active versus passive adaptive management policies.
  • Modeling of revegetation options incorporating explicit monitoring costs.
  • Evaluation of how terminal reward values and management objectives affect policy differences.

Main Results:

  • Active and passive adaptive solutions similarity is influenced by the value of terminal rewards and the choice of management objective.
  • Explicitly incorporating monitoring costs alters the perspective on how management objectives and terminal rewards impact learning.
  • Optimal monitoring states do not always align with states where active and passive management strategies diverge.

Conclusions:

  • Spending resources on monitoring is justified only when the expected benefits of considered options are comparable and the learning payoff is substantial.
  • The study provides a nuanced understanding of when to invest in active learning within adaptive management frameworks.
  • Case study results highlight the importance of context-specific factors in optimizing environmental management decisions.