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What is Conservation Biology?01:57

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Eliciting expert knowledge in conservation science.

Tara G Martin1, Mark A Burgman, Fiona Fidler

  • 1CSIRO Ecosystem Sciences, Ecoscience Precinct, GPO Box 2583 Brisbane, Queensland 4001, Australia. Tara.Martin@csiro.au

Conservation Biology : the Journal of the Society for Conservation Biology
|January 28, 2012
PubMed
Summary
This summary is machine-generated.

Expert knowledge is crucial for conservation decisions due to data limitations. Structured elicitation techniques improve the accuracy and reliability of expert judgments, ensuring uncertainty is captured effectively.

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Area of Science:

  • Conservation science
  • Decision-making under uncertainty

Background:

  • Expert knowledge is widely utilized in conservation due to complex problems and limited data.
  • Expert judgments are predictions made by subject matter specialists.

Purpose of the Study:

  • To outline a structured approach for eliciting expert knowledge in conservation.
  • To enhance the accuracy, information content, and uncertainty representation of expert judgments.

Main Methods:

  • A five-step expert elicitation process: defining use, determining elicitation scope, designing the process, performing elicitation, and encoding information.
  • Considerations for working with multiple experts, combining judgments, minimizing bias, and verifying accuracy.
  • Examination of four key aspects: study design, elicitation design, method, and output for comprehensiveness.

Main Results:

  • Structured elicitation techniques can significantly improve the quality of expert judgment.
  • Proper encoding translates expert knowledge into usable quantitative statements for models or decisions.
  • Rigorous methods ensure the reliability of expert knowledge, akin to empirical data.

Conclusions:

  • Adopting structured expert elicitation practices is vital for robust conservation science and decision-making.
  • Careful design and execution of elicitation processes are necessary to maximize the value of expert knowledge.
  • The reliability of expert knowledge is directly proportional to the rigor of the elicitation process.