A Bayesian framework for assessing extinction risk based on ordinal categories of population condition and projected landscape change. (January 2021)

• Record Type: Journal Article
• Authors: Fitzgerald, Daniel B.; Henderson, Andrew R.; Maloney, Kelly O.; Freeman, Mary C.; Young, John A.; Rosenberger, Amanda E.; Kazyak, David C.; Smith, David R.
• Is Part Of: Biological conservation. Volume 253(2021)
• Publication Date: 2021-01
• Subjects: Endangered species act -- Freshwater mussel -- Land-use change -- Markov chain Monte Carlo -- Multispecies assessment -- Ordinal regression; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2020.108866
• Languages: English
• ISSNs: 0006-3207

Abstract

Abstract: 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 >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.