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A guide to eliciting and using expert knowledge in Bayesian ecological models.

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Expert knowledge in ecology can improve conservation models when data is scarce. This study guides researchers on eliciting and incorporating expert opinion into Bayesian models to enhance decision-making.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Bayesian Modeling

Background:

  • Expert knowledge is increasingly used in conservation decision-making, especially when empirical data are limited.
  • Researchers often lack guidance on effectively utilizing expert opinion in ecological models.
  • Challenges include determining the utility of expert opinion, selecting elicitation methods, and translating insights into Bayesian priors.

Purpose of the Study:

  • To provide guidance on transparently and credibly incorporating expert knowledge into ecological models.
  • To aid natural resource and conservation decision-making by leveraging expert insights.
  • To address the need for structured approaches to expert knowledge elicitation in ecology.

Main Methods:

  • Illustrating decisions with two real ecological case studies.
  • Examining the impact of expert knowledge on Bayesian model priors.
  • Developing methods to minimize potential bias in expert opinion elicitation.

Main Results:

  • Demonstrated a framework for using expert knowledge in ecological modeling.
  • Showcased how expert opinion can be translated into prior distributions for Bayesian analysis.
  • Highlighted strategies for mitigating bias in expert-derived priors.

Conclusions:

  • Expert knowledge, when rigorously elicited and incorporated, can enhance ecological model precision.
  • Credible use of expert opinion can facilitate cost-effective and informed conservation decisions.
  • Recommendations are provided for the effective integration of expert opinion in ecological research.