Bayesreef：一个模拟珊瑚礁生长对环境变化和生物动力学响应的贝叶斯推断框架
Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics
摘要：
估算地质时期环境过程对垂直礁发育的影响是一项非常具有挑战性的任务。pyReef-Core是一个确定性的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增生和组合变化。我们提出了一个贝叶斯框架，称为贝叶斯礁的估计和不确定性量化的参数pyReef核心，代表环境条件影响珊瑚组合的地质时间尺度上的增长。我们证明了多峰后验分布的存在，并调查使用马尔可夫链蒙特卡罗（MCMC）方法，其中包括并行回火MCMC采样的挑战。我们使用合成的珊瑚礁核心调查的基本问题，然后将该方法应用于选定的珊瑚礁核心在澳大利亚大堡礁。结果表明，Bayesreef准确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。
英文摘要：
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.
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