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.

由于盐沼环境中的有孔虫与潮汐水平有很强的关系，因此它们已广泛用于定量的相对海平面重建中。然而，盐沼有孔虫的时间和空间变异性对定量重建的影响仍然不受限制。在这里，我们对新泽西州四个潮间带监测站的有孔虫进行了三年的监测研究，这些监测站来自高沼地环境，历时三年，其中包括几次极端天气（温度，降水和风暴潮）事件。我们从每个站点按季节（每年四次）采样四个重复样本，总共188个样本。死亡的有孔虫组合被分成四个特定地点的组合。在考虑到站间有孔虫随时间变化的系统趋势之后，监测站上有孔虫组合的分布可以解释约87％的剩余变异，而约13％可以用重复样本之间的时间和/或空间变异来解释。我们应用贝叶斯传递函数来估计四个监测站的海拔。每个站点的所有样本均在与该站点观测到的海拔高度相符的95％不确定区间内预测了海拔估计。将样本组合到重复和季节性汇总数据集中可以降低海拔估计的不确定性，而秋季和冬季汇总数据集的下降幅度最大。有关现代有孔虫分布的时间和空间变异性的信息通过先验性有孔虫的变异性先验被正式纳入贝叶斯传递函数中，并被应用于新泽西州的一个普通时代相对海平面记录。使用信息性和非信息性先验的古沼泽海拔估计值和不确定性的平均差异很小（分别小于0.01 m和0.01 m）。即使在极端天气事件中，死有孔虫组合在时间和较小的空间尺度上也保持一致。因此，即使考虑到现代有孔虫的变异性，在高沼泽环境中基于有孔虫的相对海平面重建仍然是可靠且可重现的。使用信息性和非信息性先验的古沼泽海拔估计值和不确定性的平均差异很小（分别小于0.01 m和0.01 m）。即使在极端天气事件中，死有孔虫组合在时间和较小的空间尺度上也保持一致。因此，即使考虑到现代有孔虫的变异性，在高沼泽环境中基于有孔虫的相对海平面重建仍然是可靠且可重现的。使用信息性和非信息性先验的古沼泽海拔估计值和不确定性的平均差异很小（分别小于0.01 m和0.01 m）。即使在极端天气事件中，死有孔虫组合在时间和较小的空间尺度上也保持一致。因此，即使考虑到现代有孔虫的变异性，在高沼地环境中基于有孔虫的相对海平面重建仍然是可靠且可重复的。