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Related Concept Videos

The Fossil Record02:56

The Fossil Record

The fossil record documents only a small fraction of all organisms that have ever inhabited Earth. Fossilization is a rare process, and most organisms never become fossils. Moreover, the fossil record only exhibits fossils that have been discovered. Nevertheless, sedimentary rock fossils of long-lived, abundant, hard-bodied organisms dominate the fossil record. These fossils offer valuable information, such as an organism's physical form, behavior, and age. Studying the fossil record helps...

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Efficient Automatic Pollen Recognition From Fossil Pollen Samples: A High-Resolution Example Record From Palaeolake

Martin Theuerkauf1,2, Alexander Gillert3

  • 1Institute of Ecology Leuphana University Lüneburg Lüneburg Germany.

Ecology and Evolution
|June 24, 2026
PubMed
Summary

A new TOFSI approach uses two neural networks for automated pollen analysis from lake sediments, significantly reducing processing time and improving accuracy for ecological reconstructions.

Keywords:
HoloceneLateglacialTOFSIconvolutional neural networkspollen analysisvegetation history

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Area of Science:

  • Paleoecology
  • Palynology
  • Computational Biology

Background:

  • Pollen analysis is vital for reconstructing past environments but traditionally manual and time-intensive.
  • Existing automated methods using neural networks struggle with real-world fossil pollen data from sediments.
  • High-resolution, large-scale pollen analysis has been limited by manual processing constraints.

Purpose of the Study:

  • To introduce and evaluate the TOFSI (Time-of-Flight Secondary Ionization) approach for automated pollen detection and classification.
  • To apply TOFSI to a high-resolution lake sediment sequence for detailed paleoecological reconstruction.
  • To assess the accuracy and efficiency of TOFSI compared to traditional manual methods.

Main Methods:

  • Development of a novel approach (TOFSI) employing two neural networks for object detection and subsequent classification.
  • Training a model to recognize 48 distinct pollen, spore, and Non-Pollen Palynomorph (NPP) classes.
  • Application of the TOFSI approach to a 1 cm resolution lake sediment core.

Main Results:

  • TOFSI achieved high performance (≥0.9 recall and precision at 0.5 confidence) for well-represented classes in training data.
  • Performance showed a decline for classes with fewer than approximately 100 training examples.
  • The method demonstrated excellent accuracy in detecting and classifying multiple pollen, spore, and NPP types in lake sediment.

Conclusions:

  • TOFSI enables accurate, automated pollen analysis in lake sediments when sufficient training data is available.
  • The approach offers both fully automated (limited resolution) and semi-automatic (full resolution with manual revision) workflows.
  • TOFSI significantly reduces analysis time, increases count sums, enhances statistical reliability, and offers practical improvements for palynologists.