Andrew R Henderson Kelly O. Maloney Mary Freeman John A. Young Amanda E. Rosenberger David C. Kazyak David R. Smith Daniel Bruce Fitzgerald 2020 <div id="abstracts" class="Abstracts u-font-serif"><div id="ab0005" class="abstract author" lang="en"><div id="as0005"><p id="sp0035">Many at-risk species lack standardized surveys across their range or quantitative data capable of detecting demographic trends. As a result, extinction risk assessments often rely on ordinal categories of risk based on explicit criteria or expert elicitation. This study demonstrates a Bayesian approach to assessing extinction risk based on this common data structure, using three freshwater mussel species being considered for listing under the US Endangered Species Act. The probability that a population is classified under each risk category was modeled as a function of projected landscape change using ordered probit regression, assuming observed categories reflect a latent, continuous probability of persistence. All three species were more likely than not (mean probability &gt;0.5) to be classified as extirpated or low condition throughout their range based on effects of urban development and hydrologic alteration. Spatial variation in estimates revealed strongholds and high-risk areas relevant to conservation decision making. Projected change in probabilities of each risk category based on multiple land-use and climate models was generally small relative to high baseline risk resulting from past landscape changes. Assessing extinction risk based on probabilities of ordinal condition as a function of landscape patterns may provide a flexible and robust approach for many at-risk taxa by adjusting species' demographic criteria to match relative risk categories, following standardized criteria, or using expert elicitation for data-deficient species. This approach provides decision makers with a useful measure of uncertainty around ordinal classifications and provides a framework for estimating future risk based on projections of anthropogenic stressors.</p></div></div></div><ul id="issue-navigation" class="issue-navigation u-margin-s-bottom u-bg-grey1"></ul> application/pdf 10.1016/j.biocon.2020.108866 en Elsevier A Bayesian framework for assessing extinction risk based on ordinal categories of population condition and projected landscape change article