Application of confocal laser microscopy for identification of modern and fossil pollen grains, an example in palm Mauritiinae - Collevatti, Rosane G.; Castañeda, Marcela; Silva-Caminha, Silane A.F.; Jaramillo, Carlos (Review of palaeobotany and palynology, 2024)
Confocal scanning laser microscopy (CSLM) is becoming a powerful tool for palynological studies. CSLM allows palynomorph image sectioning, internal and surface structures visualization, and 3D reconstruction at a higher resolution than standard light microscopy without extra processing. CSLM images are suitable for several image analysis techniques that could help improve the accuracy and reproducibility of taxa identification. Here, using the palm subtribe Mauritiinae (Arecaceae: Calamoideae: Lepidocaryeae) as a model group, we identify modern and fossil pollen grains using CSLM images coupled with ImageJ/Fiji 1.54f plugins and machine learning statistical analyses. Modern taxa pollen grains including Lepidocaryum tenue Mart., Mauritia flexuosa L.f., Mauritiella armata (Mart.) Burret and Mauritiella aculeata (Kunth) Burret were obtained from Smithsonian Tropical Research Institute (STRI) pollen collection or herbarium exsiccates. Fossil pollen of Grimsdalea magnaclavata Germeraad et al. 1968, and Mauritiidites franciscoi (van der Hammen) van der Hammen & Garcia de Mutis 1966, both from Miocene, and Mauritia pollen type from Holocene were obtained from STRI collection. We measured nine shape and exine quantitative parameters, and one qualitative parameter (pollen aperture). Pollen volume was the most important variable (28.270 mean decrease accuracy), followed by pollen aperture (15.003), Skewness (13.466), and spine density (10.246). The machine learning analysis, which included CART and Random Forests, correctly identified both fossil and extant grains. CSLM and the quantitative analysis of morphological traits are a new frontier in palynological studies. •We developed a framework for fossil and modern pollen grain identification.•We used confocal laser scanning microscopy images coupled with open-source image processor software, and machine learning analysis.•Pollen’s shape and surface structure showed higher accuracy in machine learning classification than light microscopy morphological traits.•Our framework opens new perspective for automate and digital pollen identification in any plant group.•Our analysis pipeline will become powerful tools to unravel the true potential of palynology.