Value of information of managing key threatening processes in NSW

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.\nLineage: 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). \n\nThe 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.

本数据集包含经匿名化处理、采用专家征询法（expert elicitation）获取的相关数据，涉及《2016年新南威尔士州生物多样性保护法》（NSW Biodiversity Conservation Act 2016）中列明的19种主要威胁过程（Key Threatening Processes, KTP）的管理有效性。依托该数据集及配套的R语言与Matlab代码，可生成两项核心信息：(1) 当前不确定性条件下的预期管理有效性；(2) 消除管理相关不确定性后的预期价值。相关分析的完整细节见下述报告：Nicol S、Brazill-Boast J、Gorrod E、McSorley A、Peyrard N、Chadès I (2018). 《基于信息价值优先开展主要威胁过程与保护物种的研究与管理》，联邦科学与工业研究组织（CSIRO），布里斯班。

数据溯源：原始数据由新南威尔士州的物种与威胁管理专家协作，通过专家征询法采集。本研究已获得CSIRO人类研究伦理委员会的伦理批准（项目批准号：006/18）。

本项目采用专家征询法，评估用于优化新南威尔士州19种列明的主要威胁过程管理的信息价值。研究团队共通过邮件邀请261位专家参与，最终有65位专家提供了评估数据。研究人员依据物种对主要威胁过程的相似响应特征，将其划分为功能群并展示给各位专家。针对每一种主要威胁过程与功能群，我们从专家处获取三项信息：(1) 采用最佳实践管理可有效管控该威胁过程的可能性；(2) 未实施任何管理措施时，对应物种功能群的平均存续概率；(3) 实施最佳实践管理时，对应物种功能群的平均存续概率。在每次评估环节中，专家需提供估值的下限、上限及最佳估计值，同时给出其对真实值落在该区间内的置信度。随后，我们为每项评估结果拟合概率分布，分别用以表征给定管理有效性水平下的有效管理可能性，以及功能群的响应可能性。基于这些分布，我们可计算出在两种情境下，针对每种威胁过程开展管理所能为各功能群带来的预期存续收益：(i) 当前认知水平下；(ii) 完全认知情境下（即消除管理结果的不确定性）。最后，我们为每个功能群计算得到完全信息预期价值（Expected Value of Perfect Information, EVPI），该指标可量化消除管理结果不确定性所能带来的潜在收益。完全信息预期价值较高的主要威胁过程，是开展管理有效性研究的优质候选对象。