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

我们对过去~1亿年间地球气候的大部分知识都是从一种叫做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，生活在上层水柱（浮游生物）或海底（底栖生物）。在它们的生命周期结束后，沙粒大小的方解石壳成为化石，并对海洋沉积物做出了重大贡献。这些丰富的化石记录，加上环境参数与壳化学之间的关系，使有孔虫成为一个主要的气候档案。虽然从底栖和浮游有孔虫壳中收集的化学和同位素数据提供了地球过去气候变化的基本一致的记录，但这些古记录可能略有偏差，因为化石壳的化学成分可能在沉积柱中沉积数百万年后发生了变化。对这种偏差的精确评估可以显著改善具有社会重要性的关键应用，如过去海平面或冰盖的模式模拟，因为这些模式强烈依赖于古气候数据的质量。该项目将采用先进的技术分析工具来记录由于化石壳的变化而导致的化学成分偏差的程度。该项目将支持和培训一名女研究生进行研究。项目外展活动包括为Lamont Core Repository和Open House提供博物馆级别的展示。先前的研究表明，有孔虫壳中的某些区域比剩余的材料保存得更好，从而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是无法通过传统的分析方法，需要分析整个壳。因此，传统的“全壳”数据代表了不同代壳方解石的平均化学成分，可能具有较大的保存范围。最近的创新结合了扫描电子显微镜（SEM）、阴极发光（CL）光谱法、二次离子质谱法（SIMS）和电子探针微分析仪（EPMA）等高倍成像技术，现在可以对单个有孔虫壳中保存完好和改变的结构域进行鉴定和化学分析。该项目将把这种最先进的方法应用于与气候模式模拟相关的气候事件的底栖有孔虫壳，如末次冰期-间冰期和上新世中期暖期（MPWP），一个潜在的气候模拟~3.2 - 2.9亿年前。这项研究将为模型模拟提供更可靠的古气候数据，此外，与传统测量的比较将允许对已发表记录中的潜在偏差进行量化，从而建立校正。这是第一次以前所未有的详细程度调查底栖有孔虫的潜在变化的研究，预期的结果将是编制更准确的古气候记录，这对长期预测的模式模拟性能有直接影响。

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.