Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

Abstract

Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia, Berlinia, and Anthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies.

Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring

“…Deep learning models have shown similar accuracy rates to ours on larger and more varied pollen datasets as well, but these either focussed on the family level 28 or on insect-collected pollen for honey analysis 29,30 . Increasing the taxonomic resolution of pollen grains has been achieved by incorporating an extensively trained deep learning model with super-resolution microscopy on a case study of fossil pollen 31 . Similarly, incorporating SEM images has been found to allow for highly accurate distinction of pollen types 32 .…”

Deep learning in deep time

“…In PNAS, Romero et al ( 9 ) present a deep-learning–based approach for classifying some of the fossil record’s most widely documented yet vexing historical material—fossil pollen ( 8 , 10 ). Paired with an ecological and climatic understanding of the distributions of plant groups today, taxonomically resolved pollen studies provide an important lens for paleobotanical diversity and data for paleoclimatic inference ( 8 , 10 ).…”

“…Paired with an ecological and climatic understanding of the distributions of plant groups today, taxonomically resolved pollen studies provide an important lens for paleobotanical diversity and data for paleoclimatic inference ( 8 , 10 ). Romero et al ( 9 ) show how this record can be further refined with deep learning. The authors examine a locally rare but geographically widespread fossil morphospecies historically distributed through Africa and South America between 59.2 and 7.2 Ma.…”

“…Protocols and algorithms for training accurate CNNs are rapidly advancing, and transfer learning ( 13 ), a process by which pretrained CNNs are modified and adjusted for new applications, has lowered the threshold for the number of training images required, allowing relatively data depauperate implementations. Where thousands of examples per class were once needed, Romero et al ( 9 ) train a relatively accurate model with 16 extant plant genera and a mere 459 specimens.…”

“… 14 ) and was recently developed in a study of extant pollen ( 15 ). Romero et al ( 9 ) show how CNNs trained to identify modern taxa can be paired with imaging techniques to classify organisms deep in the fossil record.…”