发表机构
Hamilton Institute and Department of Mathematics and Statistics, Maynooth University; School of Mathematics and Statistics, University College Dublin; Department of Biosciences, Durham University（梅努斯大学汉密尔顿研究所与数学统计系; 都柏林大学学院数学统计学院; 杜伦大学生物科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出贝叶斯模块化框架，利用零膨胀计数成分花粉数据和贝叶斯加性回归树，结合切割后验分布与采样重要性重采样，实现古气候重建并量化不确定性。
AI 中文摘要
基于化石花粉计数的贝叶斯古气候重建依赖于现代花粉-气候校准数据集，以推断用于重建过去气候的花粉-气候关系。虽然地理上大规模的校准数据集改善了气候空间的覆盖并减少了不可靠的外推，但它们也引入了显著的异质性、结构零值和复杂的花粉-气候关系。我们提出了一个用于从计数成分花粉数据重建古气候的贝叶斯模块化框架，该框架解决了这些挑战，并提供了连贯的不确定性量化。该框架采用零和N膨胀多项逻辑正态分布来描述成分花粉计数，并结合贝叶斯加性回归树先验来模拟气候协变量之间的非线性效应和交互作用。推断通过切割后验分布进行，将分析模块化为正向模块和重建模块。正向模块使用大型现代校准数据集拟合一次，其后的不确定性被传播以从化石花粉计数中重建气候变量。对于重建模块，我们开发并比较了三种逆后验采样方案。模拟研究和现代数据集的实证验证表明，将采样重要性重采样与多重插补技术以及现代气候变量域上的连续均匀先验相结合，实现了最佳的预测性能，并为气候重建提供了校准良好的不确定性量化。在我们的动机案例研究中，我们通过从意大利南部Lago Grande di Monticchio采集的化石花粉记录重建三维气候向量，进一步展示了所提出的方法。
英文摘要
Bayesian palaeoclimate reconstruction from fossil pollen counts relies on a modern pollen-climate calibration data set to infer the pollen-climate relationships used to reconstruct past climates. While geographically large calibration data sets improve coverage of climate space and reduce unreliable extrapolation, they also introduce substantial heterogeneity, structural zeros, and complex pollen-climate relationships. We propose a Bayesian modular framework for palaeoclimate reconstruction from count-compositional pollen data that addresses these challenges, and provides coherent uncertainty quantification. The framework employs the zero-and-$N$-inflated multinomial logistic-normal distribution to describe the compositional pollen counts coupled with Bayesian additive regression tree priors to model the nonlinear effects and interactions among the climate covariates. Inference is formulated through a cut posterior distribution that modularises the analysis into forward and reconstruction modules. The forward module is fitted once using a large modern calibration data set and its posterior uncertainty is subsequently propagated to reconstruct climate variables from fossil pollen counts. For the reconstruction module, we develop and compare three inverse posterior sampling schemes. Simulation studies and empirical validation on the modern data set demonstrate that a combination of sampling importance resampling with a multiple imputation technique and a continuous uniform prior over the domain of the modern climate variables achieves the best predictive performance, with well-calibrated uncertainty quantification for the climate reconstruction. In our motivating case study, we further illustrate the proposed methodology by reconstructing a three-dimensional climate vector from fossil pollen records collected at Lago Grande di Monticchio in southern Italy.
Comments35 pages; 9 figures
