Incorporating temporal and spatial variability of salt-marsh foraminifera into sea-level reconstructions

被引:10
|
作者
Walker, Jennifer S. [1 ,7 ]
Cahill, Niamh [2 ]
Khan, Nicole S. [3 ]
Shaw, Timothy A. [4 ]
Barber, Don [5 ,6 ]
Miller, Kenneth G. [1 ,7 ]
Kopp, Robert E. [1 ,7 ]
Horton, Benjamin P. [4 ,8 ]
机构
[1] Rutgers State Univ, Dept Earth & Planetary Sci, New Brunswick, NJ 08901 USA
[2] Maynooth Univ, Dept Math & Stat, Maynooth, Kildare, Ireland
[3] Univ Hong Kong, Dept Earth Sci & Swire Marine Inst, Hong Kong, Peoples R China
[4] Nanyang Technol Univ, Earth Observ Singapore, Singapore 639798, Singapore
[5] Bryn Mawr Coll, Dept Environm Studies, Bryn Mawr, PA 19010 USA
[6] Bryn Mawr Coll, Dept Geol, Bryn Mawr, PA 19010 USA
[7] Rutgers State Univ, Inst Earth Ocean & Atmospher Sci, New Brunswick, NJ 08901 USA
[8] Nanyang Technol Univ, Asian Sch Environm, Singapore 639798, Singapore
基金
新加坡国家研究基金会;
关键词
Foraminifera; Salt marsh; Transfer function; Relative sea level; BARRIER-ISLAND EVOLUTION; INDIAN RIVER LAGOON; NORTH-CAROLINA; INTERTIDAL FORAMINIFERA; BENTHIC FORAMINIFERA; COWPEN MARSH; TEES ESTUARY; CONFIDENCE-LIMITS; ATLANTIC COAST; STANDING CROP;
D O I
10.1016/j.margeo.2020.106293
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 similar to 87% of the remaining variation, while similar to 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.
引用
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页数:17
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