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Published on: December 9, 2012
Evaluating a structured expert elicitation approach for adaptive conservation management based on lessons from
Helen J Mayfield1,2, James Brazill-Boast3, Mick Andren3
1School of the Environment, The University of Queensland, Brisbane, Queensland, Australia.
Structured expert elicitation aids adaptive management for threatened species. While predictions sometimes diverged from observed outcomes, the process improved planning and understanding in uncertain environments.
Area of Science:
- Conservation Biology
- Ecology
- Environmental Management
Background:
- Adaptive management of threatened species requires accurate predictions of intervention outcomes.
- Structured expert elicitation is a common method for generating these predictions, but empirical validation is infrequent.
Purpose of the Study:
- To evaluate the effectiveness of structured expert elicitation for adaptive management within the New South Wales Saving our Species program.
- To compare expert-predicted species responses to management interventions with observed outcomes over a five-year period.
Main Methods:
- Revisiting six species management plans developed using tailored structured elicitation guidelines.
- Experts reviewed conceptual models and compared predicted management responses with monitoring data.
- Analysis of discrepancies between predicted and observed outcomes, considering factors like new threats and intervention effectiveness.
Main Results:
- Observed outcomes closely matched expert predictions in three out of six case studies.
- Discrepancies were attributed to unanticipated threats and unexpected species responses to interventions.
- The structured elicitation process provided a clear planning framework and facilitated hypothesis generation for unexpected results.
Conclusions:
- Tailored guidelines for structured expert elicitation are valuable for supporting adaptive management.
- Documenting expert knowledge is crucial for managing species in uncertain conditions.
- The elicitation process enhanced collaborative planning and refined managers' understanding despite prediction variations.
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