Capturing expert knowledge for threatened species assessments: a case study using NatureServe conservation status ranks

Abstract

Assessments for assigning the conservation status of threatened species that are based purely on subjective judgements become problematic because assessments can be influenced by hidden assumptions, personal biases and perceptions of risks, making the assessment process difficult to repeat. This can result in inconsistent assessments and misclassifications, which can lead to a lack of confidence in species assessments. It is almost impossible to understand an expert’s logic or visualise the underlying reasoning behind the many hidden assumptions used throughout the assessment process. In this paper, we formalise the decision making process of experts, by capturing their logical ordering of information, their assumptions and reasoning, and transferring them into a set of decisions rules. We illustrate this through the process used to evaluate the conservation status of species under the NatureServe system (Master, 1991). NatureServe status assessments have been used for over two decades to set conservation priorities for threatened species throughout North America. We develop a conditional point-scoring method, to reflect the current subjective process. In two test comparisons, 77% of species’ assessments using the explicit NatureServe method matched the qualitative assessments done subjectively by NatureServe staff. Of those that differed, no rank varied by more than one rank level under the two methods. In general, the explicit NatureServe method tended to be more precautionary than the subjective assessments. The rank differences that emerged from the comparisons may be due, at least in part, to the flexibility of the qualitative system, which allows different factors to be weighted on a species-by-species basis according to expert judgement. The method outlined in this study is the first documented attempt to explicitly define a transparent process for weighting and combining factors under the NatureServe system. The process of eliciting expert knowledge identifies how information is combined and highlights any inconsistent logic that may not be obvious in subjective decisions. The method provides a repeatable, transparent, and explicit benchmark for feedback, further development, and improvement.