Advanced imaging techniques combined with in situ analyses used to assess diagenesis in benthic foraminifera

我们对过去1亿年来地球气候的大部分知识都是从称为有孔虫的海洋微生物的外壳中推断出来的。这些单细胞生物在世界海洋中无处不在，要么生活在上层水柱(浮游生物)，要么生活在海底(海底生物)。在它们的生命周期完成后，沙粒大小的方解石贝壳就会变成化石，并对海洋沉积物做出重大贡献。这种丰富的化石记录，再加上环境参数和贝壳化学之间已被充分研究的关系，使有孔虫成为主要的气候档案。虽然从海底和浮游有孔虫壳体收集的化学和同位素数据基本上一致地记录了地球过去的气候变化，但这些古记录可能略有偏差，因为化石壳体的化学成分在沉积柱中沉积了数百万年后可能会发生变化。对这一偏差的准确评估可以大大改进社会重要性的关键应用，例如过去海平面或冰盖的模型模拟，因为这些模型严重依赖于古气候数据的质量。该项目将应用先进的技术分析工具来记录化石贝壳变化造成的化学成分偏差的严重程度。该项目将支持和培训一名女研究生进行这项研究。项目外展活动包括拉蒙特核心储存库和开放之家的博物馆级展示。以前的研究表明，有孔虫贝壳中的某些区域比其余材料保存得更好，因此提供了过去环境参数的可靠记录。然而，这些保存完好的区域无法通过需要对整个贝壳进行分析的传统分析方法来获得。因此，传统的“全壳”数据代表了来自潜在大范围保存范围的不同世代贝壳方解石的平均化学成分。最近在结合使用扫描电子显微镜(SEM)、阴极发光(CL)光谱、二次离子质谱仪(SIMS)和电子探针显微分析仪(EPMA)的高倍率成像方面的创新，现在可以对单个有孔虫贝壳中保存完好和变化的区域进行识别和化学分析。该项目将对与气候模型模拟有关的气候事件，如最后一次冰川-间冰期和上新世中期暖期(MPWP)--一种潜在的3.2-2.9英里的气候模拟--的海底有孔虫贝壳应用这一最先进的方法。那是以前的事了。这项研究将为模型模拟提供更可靠的古气候数据，此外，与传统测量数据的比较将使已公布记录中的潜在偏差得以量化，从而建立校正。这是第一次以前所未有的详细程度调查海底有孔虫潜在变化的研究，预期的结果将是汇编更准确的古气候记录，这些记录对长期预测的模型模拟的性能有直接影响。

Much of our knowledge of the Earth's climate of the past ~100 million years is deduced from shells of marine microorganisms called foraminifera. These single-celled organisms are ubiquitous in the world's oceans and live either in the upper water column (planktic) or on the sea floor (benthic). After their life-cycle is completed, the sand-grain-sized calcite shells are fossilized and contribute substantially to marine sediments. This rich fossil record, combined with the well-studied relationship between environmental parameters and shell-chemistry, make foraminifera a primary climate archive. While chemical and isotopic data collected from benthic and planktic foraminifer shells provide a largely consistent record of the Earth's past climate variability, these paleorecords may be slightly biased, as the chemical composition of fossil shells may be modified after being deposited for millions of years in the sediment column. A precise assessment of this bias could significantly improve critical applications of societal importance such as model simulation of past sea level or ice sheets, as these models strongly rely on the quality of paleoclimate data. This project will apply advanced technological analytical tools to document the magnitude of this bias in chemical composition resulting from alteration of fossil shells. The project will support and train a female graduate student in the research. Project outreach activities include museum quality displays for the Lamont Core Repository and Open House.Previous studies suggest that certain domains within foraminifera shells are better preserved than the remaining material, and thus provide a robust record of past environmental parameters. However, these well-preserved domains are inaccessible by conventional analytical approaches that require analysis of whole shells. Thus, conventional "whole-shell" data represent the averaged chemical composition of different generations of shell calcite from a potentially large preservational range. Recent innovations in the combined use of high-magnification imaging by scanning electron microscope (SEM), cathodoluminescence (CL)-spectrometry, secondary ion mass spectrometry (SIMS), and electron probe microanalyzer (EPMA) now allows the identification and chemical analyses of both well-preserved and altered domains in single foraminifera shells. This project will apply this state-of-the-art approach on benthic foraminiferal shells from climate events relevant for climate model simulations, such as the last glacial-interglacial intervals and the Mid Pliocene Warm Period (MPWP), a potential climate analogue ~3.2 - 2.9 m.y. ago. This study will provide model simulations with more robust paleoclimate data, further, a comparison with conventional measurements will permit quantification of the potential bias in published records and thus establish corrections. This is the first study investigating potential alteration in benthic foraminifera with an unprecedented level of detail, and the anticipated outcome will be the compilation of more accurate paleoclimate records, which have a direct impact on the performance of model simulations for long-term forecasts.