Application of qualitative value of information to prioritize uncertainties about eastern black rail population recovery

Abstract

Natural resource management decisions are often made in the face of uncertainty. The question for the decision maker is whether the uncertainty is an impediment to the decision and, if so, whether it is worth reducing uncertainty before or while implementing actions. Value of information (VoI) methods are decision analytical tools to evaluate the benefit to the decision maker of resolving uncertainty. These methods, however, require quantitative predictions of the outcomes as a function of management alternatives and uncertainty, in which predictions which may not be available at early stages of decision prototyping. Here we describe the first participatory application of a new qualitative approach to VoI in an adaptive management workshop for Atlantic Coast eastern black rail populations. The eastern black rail is a small, cryptic marsh bird that was recently listed as federally threatened, with extremely little demographic data available. Workshop participants developed conceptual models and nine hypotheses related to the effects of habitat management alternatives on black rail demography. Here, we describe the qualitative VoI framework, how it was implemented in the workshop, and the analysis outcomes, and describe the benefits of qualitative VoI in the context of adaptive management and co-production of conservation science.

Qualitative value of information provides a transparent and repeatable method for identifying critical uncertainty

“…In our virtual workshop and QVoI scoring, we articulated management relevant hypotheses (H A ), each with an implicit null hypothesis (H 0 ) that was formally stated after the workshop. We developed hypotheses following a common format that specified a threat to the focal species, described the demographic mechanism by which they were affected, and proposed a management action that may reduce or eliminate the threat (Lawson et al, 2022). We then further refined these hypotheses following the workshop to identify a null and an alternative hypothesis and the associated preferred management actions.…”

“…We then further refined these hypotheses following the workshop to identify a null and an alternative hypothesis and the associated preferred management actions. Explicitly developing hypotheses within a paired alternative and null hypothesis framework during the workshop, rather than post‐workshop, may improve the QVoI scoring process as participants carefully consider controlled management experiments that explicitly aim to falsify the alternative (H A ) hypotheses (Lawson et al, 2022; Rushing et al, 2020).…”

“…Rather than place hypotheses into the four categories based on natural breaks in the final QVoI and Reducibility scores (e.g., Rushing et al, 2020), we used the average of the minimum and maximum final QVoI and Reducibility scores to create the four quadrants (Lawson et al, 2022). We then prioritized hypotheses based on their Pareto optimality whereby hypotheses along the frontier maximized QVoI and Reducibility.…”

“…These traditional, quantitative VoI methods each require data that may not be readily available at an early stage of decision prototyping. However, the qualitative index to the VoI (QVoI) provides an efficient method for identifying critical uncertainty that is consistent with quantitative VoI methods but asks less of the decision‐maker and analysts (Lawson et al, 2022; M. C. Runge, C. S. Rushing, J. E. Lyons, & M. A. Rubenstein, unpublished data; Rushing et al, 2020). Therefore, QVoI offers an alternative approach at the early stages of decision‐making, especially for newly listed threatened or endangered species where management decisions may be made quickly under uncertainty.…”

“…QVoI analysis is a stepwise process that includes articulating and scoring alternative hypotheses, calculating QVoI, and comparing QVoI against the expected reducibility of uncertainty for each hypothesis (Lawson et al, 2022; Rushing et al, 2020). In this approach, participants create conceptual models of the managed system and identify key uncertainties within the system that may impede the choice of management actions.…”