Bayesian ages for pollen records since the last glaciation in North America
Bayesian ages for pollen records since the last glaciation in North America
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DOI：
10.1038/s41597-019-0182-7
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发表时间：
2019-09-24
期刊：
影响因子：
9.8
通讯作者：
McGuire, Jenny L.
中科院分区：
文献类型：
--
作者：
Wang, Yue;Goring, Simon J.;McGuire, Jenny L.
陆地花粉记录丰富，分布广泛，使其成为过去植被动态的一个很好的代理。深度模型将沉积物岩芯中的花粉样本与基于样本深度和可用年代控制之间的关系的沉积年龄相关联。花粉数据的大规模综合得益于对年龄不确定性的一致处理。生成新的年龄模型有助于减少使用过时技术的遗留年龄模型的潜在工件。传统的年龄-深度模型通常用于比较目的，通过拟合日期样本之间的曲线来推断年龄。培根，贝叶斯理论的基础上，模拟沉积物的沉积过程，占两个变量的沉积速率和时间/空间的自相关性的沉积从一个样品到另一个核心。培根提供了强大的不确定性估计与不同的沉积过程的核心。我们使用培根估计花粉样品年龄从554北美沉积物芯。该数据集支持年龄深度估计，支持未来的大型时空研究，并为对跨多个核心集成的问题感兴趣的科学家消除了一个具有挑战性的计算密集型步骤。
Terrestrial pollen records are abundant and widely distributed, making them an excellent proxy for past vegetation dynamics. Age-depth models relate pollen samples from sediment cores to a depositional age based on the relationship between sample depth and available chronological controls. Large-scale synthesis of pollen data benefit from consistent treatment of age uncertainties. Generating new age models helps to reduce potential artifacts from legacy age models that used outdated techniques. Traditional age-depth models, often applied for comparative purposes, infer ages by fitting a curve between dated samples. Bacon, based on Bayesian theory, simulates the sediment deposition process, accounting for both variable deposition rates and temporal/spatial autocorrelation of deposition from one sample to another within the core. Bacon provides robust uncertainty estimation across cores with different depositional processes. We use Bacon to estimate pollen sample ages from 554 North American sediment cores. This dataset standardizes age-depth estimations, supporting future large spatial-temporal studies and removes a challenging, computationally-intensive step for scientists interested in questions that integrate across multiple cores.