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

我们对过去1亿年地球气候的大部分了解都是从被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，生活在上层水柱（南极）或海底（海底）。在它们的生命周期完成后，沙粒大小的方解石壳被石化，并对海洋沉积物有很大的贡献。这丰富的化石记录，结合环境参数和壳化学之间的关系，使有孔虫成为主要的气候档案。虽然从底栖有孔虫和南极有孔虫贝壳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，因为化石贝壳的化学成分在沉积柱中沉积数百万年后可能会发生变化。对这种偏差的精确评估可以显着改善具有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，记录由于化石贝壳的改变而造成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从事这项研究。 项目外展活动包括为拉蒙岩心库和开放日举办博物馆级展览。先前的研究表明，有孔虫壳内的某些领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”数据代表了不同世代的壳方解石的平均化学成分，从一个潜在的大范围。最近的创新组合使用的高放大倍数成像扫描电子显微镜（SEM），阴极发光（CL）光谱，二次离子质谱（西姆斯），电子探针显微分析仪（EPMA），现在允许的识别和化学分析保存完好的和改变域在单一有孔虫壳。该项目将对来自与气候模型模拟相关的气候事件的底栖有孔虫贝壳应用这种最先进的方法，例如末次冰期-间冰期间隔和上新世中期温暖期（MPWP），这是一个潜在的气候模拟~ 320 - 290万年。前这项研究将提供更强大的古气候数据的模型模拟，此外，与传统的测量比较将允许量化的潜在偏差在公布的记录，从而建立校正。这是第一项以前所未有的详细程度调查底栖有孔虫潜在变化的研究，预期成果将是汇编更准确的古气候记录，这对长期预测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.