Value of information of managing key threatening processes in NSW

Full description

This dataset contains anonymised, expert elicited data about the management effectiveness of 19 Key Threatening Processes listed under the NSW Biodiversity Conservation Act 2016. Two key pieces of information are generated using the data and the associated R and Matlab code: (1) the expected effectiveness of management under current uncertainty, and (2) the expected value of removing uncertainty about management. Full details of the analysis are contained in the report: Nicol S, Brazill-Boast J, Gorrod E, McSorley A, Peyrard N, Chadès I (2018). Prioritising research and management of key threatening processes and listed species using value of information. CSIRO, Brisbane.
Lineage: Raw data was collected by expert elicitation in collaboration with species and threat management experts from NSW. Ethics approval was granted by CSIRO Human Research Ethics (project approval number 006/18).

The project used an expert elicitation approach to evaluate the value of information for improving the management of 19 listed KTPs in NSW. A total of 261 experts were invited to contribute by email, of which 65 provided estimates. Species were allocated to functional groups based on similar responses to KTPs and presented to experts. For each KTP and functional group, we elicited three pieces of information from experts: (1) the likelihood that best-practice management would effectively manage the KTP; (2) the average probability that functional groups of species would persist if no management was undertaken; and (3) the average probability that functional groups of species would persist if best-practice management was applied. In each estimation, experts provided lower, upper and best guesses, as well as their confidence that the true value lay between the lower and upper estimates. Estimates were then fitted with probability distributions representing the likelihood of effective management and the likelihood of functional group response for a given level of management effectiveness. These distributions were used to calculate the expected gain in persistence for each functional group resulting from managing each KTP under (i) current knowledge and (ii) perfect knowledge, i.e. if uncertainty about management outcomes was eliminated. The expected value of perfect information (EVPI) was then computed for each functional group; this quantified the likely gains from removing uncertainty about management outcomes. KTPs with high EVPI are good candidates for research into management effectiveness.