Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

Allison Y. Hsiang, Anieke Brombacher, Marina C. Rillo, Maryline J. Mleneck-Vautravers, Stephen Conn, Sian Lordsmith, Anna Jentzen, Michael J. Henehan, Brett Metcalfe, Isabel S. Fenton, Bridget S. Wade, Lyndsey Fox, Julie Meilland, Catherine V. Davis, Ulrike Baranowski, Jeroen Groeneveld, Kirsty M. Edgar, Aurore Movellan, Tracy Aze, Harry J. Dowsett & 3 others C. Giles Miller, Nelson Rios, Pincelli M. Hull

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.

Original languageEnglish
Pages (from-to)1157-1177
Number of pages21
JournalPaleoceanography and Paleoclimatology
Volume34
Issue number7
Early online date23 Jun 2019
DOIs
Publication statusPublished - Jul 2019

Fingerprint

planktonic foraminifera
resource
species concept
paleotemperature
microfossil
limiting factor
train
student
climate change
machine learning

Keywords

  • convolutional neural networks
  • global community macroecology
  • marine microfossils
  • planktonic foraminifera
  • species identification
  • supervised machine learning

Cite this

Hsiang, Allison Y. ; Brombacher, Anieke ; Rillo, Marina C. ; Mleneck-Vautravers, Maryline J. ; Conn, Stephen ; Lordsmith, Sian ; Jentzen, Anna ; Henehan, Michael J. ; Metcalfe, Brett ; Fenton, Isabel S. ; Wade, Bridget S. ; Fox, Lyndsey ; Meilland, Julie ; Davis, Catherine V. ; Baranowski, Ulrike ; Groeneveld, Jeroen ; Edgar, Kirsty M. ; Movellan, Aurore ; Aze, Tracy ; Dowsett, Harry J. ; Miller, C. Giles ; Rios, Nelson ; Hull, Pincelli M. / Endless Forams : >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks. In: Paleoceanography and Paleoclimatology. 2019 ; Vol. 34, No. 7. pp. 1157-1177.
@article{237287a514054536b91ce079f8fe82c1,
title = "Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks",
abstract = "Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4{\%} of the time and included the correct name in its top three guesses 97.7{\%} of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.",
keywords = "convolutional neural networks, global community macroecology, marine microfossils, planktonic foraminifera, species identification, supervised machine learning",
author = "Hsiang, {Allison Y.} and Anieke Brombacher and Rillo, {Marina C.} and Mleneck-Vautravers, {Maryline J.} and Stephen Conn and Sian Lordsmith and Anna Jentzen and Henehan, {Michael J.} and Brett Metcalfe and Fenton, {Isabel S.} and Wade, {Bridget S.} and Lyndsey Fox and Julie Meilland and Davis, {Catherine V.} and Ulrike Baranowski and Jeroen Groeneveld and Edgar, {Kirsty M.} and Aurore Movellan and Tracy Aze and Dowsett, {Harry J.} and Miller, {C. Giles} and Nelson Rios and Hull, {Pincelli M.}",
year = "2019",
month = "7",
doi = "10.1029/2019PA003612",
language = "English",
volume = "34",
pages = "1157--1177",
journal = "Paleoceanography and Paleoclimatology",
issn = "2572-4517",
publisher = "John Wiley & Sons, Ltd",
number = "7",

}

Hsiang, AY, Brombacher, A, Rillo, MC, Mleneck-Vautravers, MJ, Conn, S, Lordsmith, S, Jentzen, A, Henehan, MJ, Metcalfe, B, Fenton, IS, Wade, BS, Fox, L, Meilland, J, Davis, CV, Baranowski, U, Groeneveld, J, Edgar, KM, Movellan, A, Aze, T, Dowsett, HJ, Miller, CG, Rios, N & Hull, PM 2019, 'Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks' Paleoceanography and Paleoclimatology, vol. 34, no. 7, pp. 1157-1177. https://doi.org/10.1029/2019PA003612

Endless Forams : >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks. / Hsiang, Allison Y.; Brombacher, Anieke; Rillo, Marina C.; Mleneck-Vautravers, Maryline J.; Conn, Stephen; Lordsmith, Sian; Jentzen, Anna; Henehan, Michael J.; Metcalfe, Brett; Fenton, Isabel S.; Wade, Bridget S.; Fox, Lyndsey; Meilland, Julie; Davis, Catherine V.; Baranowski, Ulrike; Groeneveld, Jeroen; Edgar, Kirsty M.; Movellan, Aurore; Aze, Tracy; Dowsett, Harry J.; Miller, C. Giles; Rios, Nelson; Hull, Pincelli M.

In: Paleoceanography and Paleoclimatology, Vol. 34, No. 7, 07.2019, p. 1157-1177.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Endless Forams

T2 - >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

AU - Hsiang, Allison Y.

AU - Brombacher, Anieke

AU - Rillo, Marina C.

AU - Mleneck-Vautravers, Maryline J.

AU - Conn, Stephen

AU - Lordsmith, Sian

AU - Jentzen, Anna

AU - Henehan, Michael J.

AU - Metcalfe, Brett

AU - Fenton, Isabel S.

AU - Wade, Bridget S.

AU - Fox, Lyndsey

AU - Meilland, Julie

AU - Davis, Catherine V.

AU - Baranowski, Ulrike

AU - Groeneveld, Jeroen

AU - Edgar, Kirsty M.

AU - Movellan, Aurore

AU - Aze, Tracy

AU - Dowsett, Harry J.

AU - Miller, C. Giles

AU - Rios, Nelson

AU - Hull, Pincelli M.

PY - 2019/7

Y1 - 2019/7

N2 - Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.

AB - Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.

KW - convolutional neural networks

KW - global community macroecology

KW - marine microfossils

KW - planktonic foraminifera

KW - species identification

KW - supervised machine learning

UR - http://www.scopus.com/inward/record.url?scp=85069895900&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069895900&partnerID=8YFLogxK

U2 - 10.1029/2019PA003612

DO - 10.1029/2019PA003612

M3 - Article

VL - 34

SP - 1157

EP - 1177

JO - Paleoceanography and Paleoclimatology

JF - Paleoceanography and Paleoclimatology

SN - 2572-4517

IS - 7

ER -