This study aims to evaluate whether machine learning techniques can be successfully applied to process the complex information contained within the molecular abundance distributions of plant wax n-alkane and n-alkanoic acid homologous series. We trained five vegetation identification models using plant wax chain length distributions from modern plants in the Mojave Desert (hyperarid) and the San Bernardino Mountains (conifer forest) and previously published data for macrophytes from Blood Pond (USA) and Mt Kenya (Kenya). All vegetation identification models proved accurate (mean classification accuracy = 0.81) at classifying the modern plant wax chain length distributions into desert plants, conifer and macrophyte categories. We then applied the models to fossil waxes extracted from a 76 m lacustrine sediment core drilled in Searles Valley, CA with an approximate age range of 10 to 150 kyrs (SLAPP-SRLS17) to reconstruct the proportion of desert plants, conifer woodland and lake vegetation. We compared our machine learning models with a previously published linear mixing model and validated our modelled plant type distributions by comparing the results with the archaeol caldarchaeol ecometric (ACE), a proxy for lake salinity, measured in the same core. We found a moderate positive correlation (r = 0.40) between the modelled desert plant proportion and high lake salinity in our models as well as a negative correlation (r = –0.45) between modelled macrophyte plants and ACE, validating the ability of the machine learning techniques to detect both xeric and macrophyte plant communities. Our results suggest that machine learning of plant wax molecular abundance distributions has potential to reconstruct past plant communities, given information from two compound classes and highly differentiated vegetation types.

这项研究旨在评估机器学习技术是否可以成功地用于处理植物蜡正构烷烃和正构烷烃分子丰度分布中包含的复杂信息-链烷酸同源系列。我们使用来自莫哈韦沙漠（hyperarid）和圣贝纳迪诺山脉（针叶林）的现代植物的植物蜡链长度分布训练了五个植被识别模型，并先前发布了来自血塘（美国）和肯尼亚山（肯尼亚）的大型植物的数据。在将现代植物蜡链长度分布分为沙漠植物，针叶树和大型植物类别时，所有植被识别模型均被证明是准确的（平均分类精度= 0.81）。然后，我们将模型应用于从年龄约10至150 kyrs（SLAPP-SRLS17）的加利福尼亚州塞尔斯山谷钻探的76 m湖相沉积岩心中提取的化石蜡，以重建沙漠植物，针叶林和湖泊植被的比例。我们将机器学习模型与以前发布的线性混合模型进行了比较，并通过将结果与同一岩心中测得的湖泊盐度的代名词“古生物卡尔达古醇”（ACE）进行了比较，从而验证了我们建模的植物类型分布。我们在模型中发现建模的沙漠植物比例与高盐度之间存在适度的正相关（r = 0.40），建模的大型植物与ACE之间存在负相关（r = –0.45），验证了机器学习的能力检测干性和大型植物群落的技术。我们的结果表明，从两种化合物类别和高度分化的植被类型中获得信息，对植物蜡分子丰度分布进行机器学习具有重建过去植物群落的潜力。