Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics. (March 2020)

• Record Type: Journal Article
• Authors: Pall, Jodie; Chandra, Rohitash; Azam, Danial; Salles, Tristan; Webster, Jody M.; Scalzo, Richard; Cripps, Sally
• Is Part Of: Environmental modelling & software. Volume 125(2020)
• Publication Date: 2020-03
• Subjects: Coral reefs -- Bayesian inference -- Stratigraphic forward modelling -- Multinomial likelihood -- Markov chain Monte Carlo -- MCMC; 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.2019.104610
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages in geological timescales. Weencounter multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use a synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core . Bayesreef provides insights into the complex posterior distributions of the parameters in pyReef-Core, which provides the groundwork for future research in this area. Highlights: Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes. This paper presents Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core. The results show that Bayesreef accurately estimates and provides uncertainty quantification for parameters in pyReef-Core. Bayesreef provides insights in of pyReef-Core and establishes the groundwork for future research.