Many questions relevant to conservation decision-making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecologic processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecologic applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield elicitation framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. We used the SHELF protocol remotely to assess extinction risk of three subterranean aquatic species that are being considered for listing under the U.S. Endangered Species Act. We provided experts an empirical threat assessment for each known locality over a video conference and recorded judgments on the probability of population persistence over four generations with online submission forms and R-shiny apps available through the SHELF package. Despite large uncertainty for all populations, there were key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and species’ microhabitat. The resulting probability distributions provided decision makers with a full picture of uncertainty that was consistent with the probabilistic nature of risk assessments. Discussion among experts during SHELF's behavioral aggregation stage clearly documented dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat. Our virtual implementation of the SHELF protocol demonstrated the flexibility of the approach for conservation applications operating on budgets and time lines that can limit in-person meetings of geographically dispersed experts.

中文翻译：

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利用专家知识支持《濒危物种法》对数据缺乏物种的决策


由于缺乏经验数据和潜在生态过程的复杂性，许多与保护决策相关的问题都具有极大的不确定性，导致使用结构化协议来获取专家知识的情况迅速增加。已发布的生态应用程序通常采用改进的德尔菲法，专家匿名提供判断，并使用数学聚合技术来组合判断。谢菲尔德启发框架 (SHELF) 的不同之处在于其将个人判断综合为未知量的完全指定的概率分布的行为方法。我们远程使用 SHELF 协议来评估三种地下水生物种的灭绝风险，这些物种正在考虑列入美国濒危物种法案。我们通过视频会议向专家提供了对每个已知地点的实证威胁评估，并通过在线提交表格和通过 SHELF 软件包提供的 R-shiny 应用程序记录了对人口持续四代的概率的判断。尽管所有种群都存在很大的不确定性，但基于主要威胁的空间变化、当地土地利用和管理实践以及物种的微生境，物种灭绝的风险之间存在着重大差异。由此产生的概率分布为决策者提供了与风险评估的概率性质一致的不确定性的全貌。在SHELF行为聚合阶段，专家之间的讨论清楚地记录了主要威胁（例如，开发、木材采伐、畜牧业和洞穴参观）及其与当地洞穴地质和物种栖息地的相互作用。 我们对 SHELF 协议的虚拟实施证明了该方法在预算和时间范围内运行的保护应用程序的灵活性，这可能会限制地理分散的专家的面对面会议。