Development and Application of the Branched and Isoprenoid GDGT Machine Learning Classification Algorithm (BIGMaC) for Paleoenvironmental Reconstruction
Development and Application of the Branched and Isoprenoid GDGT Machine Learning Classification Algorithm (BIGMaC) for Paleoenvironmental Reconstruction
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DOI：
10.1029/2023pa004611
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发表时间：
2023-07
影响因子：
3.5
通讯作者：
P. Martínez-Sosa;J. Tierney;L. Pérez‐Angel;I. Stefanescu;Jingjing Guo;F. Kirkels;J. Sepúlveda;F. Peterse;B. Shuman;A. Reyes
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作者：
P. Martínez-Sosa;J. Tierney;L. Pérez‐Angel;I. Stefanescu;Jingjing Guo;F. Kirkels;J. Sepúlveda;F. Peterse;B. Shuman;A. Reyes
甘油二烷基甘油四醚（GDGT），古细菌类异戊二烯GDGT（isoGDGT）和细菌分支GDGT（brGDGT），已被用于古气候研究，以重建环境条件。由于GDGT产生于许多类型的环境中，它们的相对丰度也取决于沉积环境。这表明GDGT的分布也保留了有用的信息，可以更广泛地用于推断地质过去的沉积环境。在这里，我们将现有的iso和brGDGT相对丰度数据与新分析的样本相结合，生成了一个包含1，153个现代沉积环境样本的数据库。我们观察到一个强大的沉积环境和GDGT在我们的样品中的相对丰度之间的关系。该数据集用于训练和测试分支和isoGDGT机器学习分类（BIGMaC）算法，该算法基于GDGT的分布以高精度和召回率（F1 = 0.95）识别样本来自的环境。我们测试了该模型的沉积记录从长颈鹿金伯利岩管，始新世玛阿尔在subanabritic加拿大，并发现，BIGMaC重建同意独立的地层和孢粉学信息，提供了新的信息，该网站的古环境，并有助于提高其古温度重建。相比之下，我们还包括一个来自PETM年龄的Cobham褐煤的例子，作为说明算法局限性的警示性例子。我们建议，在古环境未知或正在发生变化的情况下，BIGMaC可以与其他代理人一起应用，以生成更精确的古气候记录。
Glycerol dialkyl glycerol tetraethers (GDGTs), both archaeal isoprenoid GDGTs (isoGDGTs) and bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct environmental conditions. Since GDGTs are produced in many types of environments, their relative abundances also depend on the depositional setting. This suggests that the distribution of GDGTs also preserves useful information that can be used more broadly to infer these depositional environments in the geological past. Here, we combined existing iso‐ and brGDGT relative abundance data with newly analyzed samples to generate a database of 1,153 samples from several modern sedimentary settings. We observed a robust relationship between the depositional environment and the relative abundances of GDGTs in our samples. This data set was used to train and test the Branched and isoGDGT Machine learning Classification (BIGMaC) algorithm, which identifies the environment a sample comes from based on the distribution of GDGTs with high precision and recall (F1 = 0.95). We tested the model on the sedimentary record from the Giraffe kimberlite pipe, an Eocene maar in subantarctic Canada, and found that the BIGMaC reconstruction agrees with independent stratigraphic and palynological information, provides new information about the paleoenvironment of this site, and helps improve its paleotemperature reconstruction. In contrast, we also include an example from the PETM‐aged Cobham lignite as a cautionary example that illustrates the limitations of the algorithm. We propose that in cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimate records.