Population viability analysis using Bayesian networks. (January 2022)

• Record Type: Journal Article
• Authors: Penman, Trent D.; McColl-Gausden, Sarah C.; Marcot, Bruce G.; Ababei, Dan A.
• Is Part Of: Environmental modelling & software. Volume 147(2022)
• Publication Date: 2022-01
• Subjects: Bayesian network -- Population viability analysis -- Demographic modeling -- Hip pocket frog -- Squirrel glider -- Giant burrowing frog; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105242
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Traditional population viability analysis (PVA) does not address the degree of measurement error or spatial and temporal variability of vital rate parameters, potentially leading to inappropriate conservation decision-making. We provide a methodology of applying Bayesian network (BN) modeling to PVA addressing these considerations, particularly for species with complex stage-class structures. We provide examples of three species from eastern Australia - hip pocket frog ( Assa darilingtoni ), squirrel glider ( Petaurus norfolcensis) and giant burrowing frog ( Heleioporus australiacus), comparing traditional matrix-based PVA with BN model analyses of mean stage abundance, quasi-extinction probability, and interval threshold extinction risk. Both approaches project similar population sizes, but BN PVA gave more clearly identifiable thresholds of population changes and extinction levels. The PVA BN uniquely represents complex stage-class structures and in a single network, including variation and uncertainty propagation of vital rates, to better inform conservation management decisions. Highlights: Conservation decision-makers rely on results modeling viability of at-risk species. Traditional modeling does not address uncertainty propagation and temporal changes. A Bayesian network approach solves this for species with complex life histories. Our approach more clearly identifies population thresholds and extinction levels. It shows viability as probabilities for use in conservation risk management.