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 on geological timescales. We demonstrate the existence of multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use 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 the environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of parameters in pyReef-Core, which provides the groundwork for future research in this area.

中文翻译：

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Bayesreef：用于模拟珊瑚礁生长以响应环境变化和生物动力学的贝叶斯推理框架

估计地质时​​期环境过程对垂直珊瑚礁发育的影响是一项非常具有挑战性的任务。pyReef-Core 是一种确定性碳酸盐岩地层正演模型，旨在模拟关键的生物和环境过程，这些过程决定了化石礁钻芯中的垂直礁石增生和组合变化。我们提出了一个称为 Bayesreef 的贝叶斯框架，用于 pyReef-Core 中参数的估计和不确定性量化，这些参数代表影响地质时间尺度上珊瑚组合生长的环境条件。我们证明了多模态后验分布的存在，并研究了使用马尔可夫链蒙特卡罗 (MCMC) 方法进行采样的挑战，其中包括并行回火 MCMC。我们使用合成珊瑚礁核心来研究基本问题，然后将该方法应用于来自澳大利亚大堡礁的选定珊瑚礁核心。结果表明，Bayesreef 准确地估计并提供了所选参数的不确定性量化，这些参数代表了 pyReef-Core 中的环境和生态条件。Bayesreef 提供了对 pyReef-Core 中参数的复杂后验分布的见解，这为该领域的未来研究奠定了基础。