Incorporating temporal and spatial variability of salt-marsh foraminifera into sea-level reconstructions
Incorporating temporal and spatial variability of salt-marsh foraminifera into sea-level reconstructions
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将盐沼有孔虫的时间和空间变化纳入海平面重建
DOI：
10.1016/j.margeo.2020.106293
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发表时间：
2020
期刊：
影响因子：
2.9
通讯作者：
Horton, Benjamin P.
中科院分区：
文献类型：
--
作者：
Walker, Jennifer S.;Cahill, Niamh;Khan, Nicole S.;Shaw, Timothy A.;Barber, Don;Miller, Kenneth G.;Kopp, Robert E.;Horton, Benjamin P.
盐沼环境中的有孔虫由于与潮位密切相关而被广泛应用于定量相对海平面重建。然而，盐沼有孔虫的时间和空间变化对定量重建的影响仍然不受限制。在这里，我们从新泽西州的四个潮间带监测站对高地沼泽环境中的有孔虫进行了为期三年的监测研究，其中包括几种极端天气(温度、降水和风暴潮)事件。我们从每个站点季节性地(每年四次)采集了四个重复样本，总计188个样本。死亡的有孔虫组合被划分为4个特定地点的组合。在考虑了站点间有孔虫随时间变化的系统趋势后，监测站间有孔虫组合的分布可解释剩余变化的约87%，而重复样本之间的时间和/或空间变化可解释约13%。我们应用贝叶斯传递函数来估计四个监测站的高程。来自每个站的所有样本预测的海拔估计值在95%的不确定区间内与该站的观测海拔一致。将样本组合成重复和季节性聚合数据集降低了海拔估计的不确定性，秋季和冬季的聚合数据集的降幅最大。关于现代有孔虫分布的时间和空间变异性的信息通过提供信息的有孔虫变异性先验被正式纳入贝叶斯传递函数，并被应用于新泽西州共同时代的相对海平面记录。使用信息性先验和非信息性先验在古玛什海拔估计和不确定性方面的平均差异很小(分别为0.01百万米和0.01百万米)。死亡的有孔虫组合在时间和小空间尺度上保持一致，即使在极端天气事件期间也是如此。因此，即使考虑到现代有孔虫的可变性，基于有孔虫的从高沼泽环境中重建的相对海平面仍保持健壮和可重复性。
Foraminifera from salt-marsh environments have been used extensively in quantitative relative sea-level reconstructions due to their strong relationship with tidal level. However, the influence of temporal and spatial variability of salt-marsh foraminifera on quantitative reconstructions remains unconstrained. Here, we conducted a monitoring study of foraminifera from four intertidal monitoring stations in New Jersey from high marsh environments over three years that included several extreme weather (temperature, precipitation, and storm surge) events. We sampled four replicates from each station seasonally (four times per year) for a total of 188 samples. The dead foraminiferal assemblages were separated into four site-specific assemblages. After accounting for systematic trends in changes in foraminifera over time among stations, the distribution of foraminiferal assemblages across monitoring stations explained ~87% of the remaining variation, while ~13% can be explained by temporal and/or spatial variability among the replicate samples. We applied a Bayesian transfer function to estimate the elevation of the four monitoring stations. All samples from each station predicted an elevation estimate within a 95% uncertainty interval consistent with the observed elevation of that station. Combining samples into replicate- and seasonal-aggregate datasets decreased elevation estimate uncertainty, with the greatest decrease in aggregate datasets from Fall and Winter. Information about the temporal and spatial variability of modern foraminiferal distributions was formally incorporated into the Bayesian transfer function through informative foraminifera variability priors and was applied to a Common Era relative sea-level record in New Jersey. The average difference in paleomarsh elevation estimates and uncertainties using an informative vs uninformative prior was minimal (<0.01 m and 0.01 m, respectively). The dead foraminiferal assemblages remained consistent on temporal and small spatial scales, even during extreme weather events. Therefore, even when accounting for variability of modern foraminifera, foraminiferal-based relative sea-level reconstructions from high marsh environments remain robust and reproducible.
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