DOI: 10.1111/cobi.70348 ISSN: 0888-8892

Evaluating a structured expert elicitation approach for adaptive conservation management based on lessons from five years in practice

Helen J. Mayfield, James Brazill‐Boast, Mick Andren, Michiala Bowen, Adam Fawcett, Trent Forge, Luke Foster, Ross Goldingay, Paul Hillier, Meagan Hinds, Simon Lee, Erica Mahon, Martine Maron, Doug Mills, Thomas Rowell, Stephanie Stuart, Caren Taylor, Grant Webster, Nicole A. Hansen

Abstract

Adaptive management of threatened species relies on having ex ante estimates of species’ responses to different interventions. Structured expert elicitation is often used to generate these estimates, but comparisons of these expert‐predicted outcomes with observed results are rare. This study aims to evaluate the utility of expert elicitation for adaptive management in the New South Wales Saving our Species program in Australia by revisiting six species management plans that were generated from tailored structured elicitation guidelines developed five years prior. Each species’ management plan included a defined scope, conceptual model, monitoring indicators, and estimated response to management curves under different scenarios. Experts reviewed the conceptual models after five years of management and compared the predicted response to management with observed monitoring data. In three of the six case studies, observed outcomes closely matched predictions. Where predictions diverged, factors such as unanticipated new threats and unexpected responses to interventions potentially contributed to discrepancies. However, in all cases, the structured approach provided a clear logic for planning, enabling managers to systematically refine their understanding. The conceptual models and response curves were valuable for collaborating, communicating, and generating hypotheses for unexpected results. This work demonstrates the value of tailored guidelines in supporting adaptive management processes and showcases the value of documenting expert knowledge in management settings with high uncertainty.

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