Utilizing probability estimates from machine learning and pollen to understand the depositional influences on branched GDGT in wetlands, peatlands, and lakes

作者：Amy Cromartie, Cindy De Jonge, Guillemette Ménot, Mary Robles, Lucas Dugerdil, Odile Peyron, Marta Rodrigo‐Gámiz, Jon Camuera, María J. Ramos‐Román, Gonzalo Jiménez‐Moreno, Claude Colombié, Lilit Sahakyan, Sébastien Joannin · 发表于：Biogeosciences · 年份：2025 · DOI：10.5194/bg-22-7687-2025 · 被引用次数：1 · 研究领域：Geology and Paleoclimatology Research、Environmental DNA in Biodiversity Studies、Peatlands and Wetlands Ecology

Abstract. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are critical molecular biomarkers for the quantitative reconstruction of past environments, ambient temperature, and pH across various archives. However, numerous issues persist that limit their application. The distribution of brGDGTs varies significantly based on provenance, resulting in biases in environmental reconstructions that rely on fractional abundances and derived indices, such as MBT5ME′. This issue is especially significant in shallow lakes, wetlands, and peatlands, where ecosystems are sensitive to diverse environmental and climatic factors. Recent advancements, such as machine learning techniques, have been developed to identify changes in provenance; however, these techniques are insufficient for detecting mixed environments. The probability estimates derived from five machine learning algorithms are employed here to detect provenance changes in brGDGT downcore records and to identify periods of mixed provenance. A new global modern database (n=2031) was compiled to train, validate, test, and apply these algorithms to two sedimentary records. Our findings are corroborated by pollen, non-pollen palynomorphs, and X-ray fluorescence (XRF) obtained from the same sedimentary core sequence. These microfossil and geochemical proxies are utilized to discuss changes in provenance, hydrology, and ecology that influence brGDGT provenance. Probability estimates derived from random forest with a sigmoid cal...