# Artificial Intelligence in Radiolarian Fossil Identification;
Taxonomic, Biostratigraphic and Evolutionary
implications

Veronica Carlsson

► To cite this version:

Veronica Carlsson. Artificial Intelligence in Radiolarian Fossil Identification; Taxonomic, Biostratigraphic and Evolutionary implications. Paleontology. Université de Lille, 2023. English. NNT: tel-04471671v2

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https://theses.hal.science/tel-04471671v2

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# Thèse de doctorat

Université de Lille

Sciences de la Terre et l'Univers

Veronica Carlsson

CNRS UMR 8198 – Evo-Eco-Paléo & CNRS UMR 9189 – CRISTAL

## Artificial Intelligence in Radiolarian Fossil Identification

*Taxonomic, Biostratigraphic and Evolutionary implications*

## Détermination des Radiolaires Fossiles à l'aide de l'Intelligence Artificielle

*Implications Taxonomiques, Biostratigraphiques et Évolutives*

### Membres du jury de la soutenance de thèse de Veronica Carlsson le 8 décembre 2023, à Lille :

Pr. Taniel Danelian

Université de Lille, France

Directeur de thèse

Pr. Pierre Boulet

Université de Lille, France

Co-directeur de thèse

Pr. Cordey Fabrice

Université de Lyon 1

Rapporteur

Dr. Thibault de Garidel-Thoron

CNRS, Université d'Aix-Marseille

Rapporteur

Pr. François Danneville

Université de Lille, France

Président du jury

Dr. Allison Hsiang

Université de Stockholm, Suède

Examinatrice

Pr. Rie Hori

Université de Ehime, Japon

Examinatrice

Pr. Catherine Crônier

Université de Lille, France

Examinatrice

Dr. Philippe Devienne

CNRS, Université de Lille

Invité
<figure><img src="image_2.png" /></figure>
<table><tr><td>Acknowledgments</td><td>7</td></tr></table>

<table><tr><td>CHAPTER 1: Introduction</td><td>8</td></tr></table>

<table><tr><td>1.1 The essence of this research</td><td>8</td></tr></table>

<table><tr><td>1.1.1 The middle Eocene</td><td>9</td></tr></table>

<table><tr><td>1.1.2 What are radiolaria?</td><td>9</td></tr></table>

<table><tr><td>1.1.3 From living organisms to several million-year-old preserved fossils</td><td>11</td></tr></table>

<table><tr><td>1.1.4 Taxonomic importance of radiolaria</td><td>12</td></tr></table>

<table><tr><td>1.1.5 Radiolaria used in biostratigraphy</td><td>12</td></tr></table>

<table><tr><td>1.1.6 Geologic setting of ODP Leg 207</td><td>15</td></tr></table>

<table><tr><td>1.1.7 The importance of Radiolaria for paleoenvironments and paleoceanography</td><td>17</td></tr></table>

<table><tr><td>1.1.8 Machine learning techniques and Artificial Intelligence</td><td>18</td></tr></table>

<table><tr><td>1.1.9 Earlier research using AI in image recognition in micropaleontology</td><td>20</td></tr></table>

<table><tr><td>1.2 Thesis objectives and structure</td><td>21</td></tr></table>

<table><tr><td>PART 1: ARTIFICIAL INTELLIGENCE APPLIED TO INDIVIDUAL SPECIES</td><td>24</td></tr></table>

<table><tr><td>CHAPTER 2: Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (Polycystine radiolaria)</td><td>24</td></tr></table>

<table><tr><td>Author contributions</td><td>25</td></tr></table>

<table><tr><td>Abstract</td><td>25</td></tr></table>

<table><tr><td>2.1 Introduction</td><td>26</td></tr></table>

<table><tr><td>2.2 Materials</td><td>30</td></tr></table>

<table><tr><td>2.3 Methods</td><td>37</td></tr></table>

<table><tr><td>2.3.1 Manual picking of individual Podocyrtis specimens</td><td>37</td></tr></table>

<table><tr><td>2.3.2 Image acquisition and processing</td><td>38</td></tr></table>

<table><tr><td>2.3.3 Datasets</td><td>40</td></tr></table>

<table><tr><td>2.3.4 MobileNet convolutional neural networks</td><td>41</td></tr></table>

<table><tr><td>2.4 Results</td><td>43</td></tr></table>

<table><tr><td>2.4.1 CNN accuracies</td><td>43</td></tr></table>

<table><tr><td>2.4.2 Confusion matrices</td><td>44</td></tr></table>

<table><tr><td>2.4.3 Testing the predictive models</td><td>49</td></tr></table>

<table><tr><td>2.5 Discussion</td><td>50</td></tr></table>

<table><tr><td>2.5.1 MobileNet performance and accuracy</td><td>50</td></tr></table>

<table><tr><td>2.5.2 Species choice and their image properties</td><td>55</td></tr></table>

<table><tr><td>2.6 Conclusions</td><td>56</td></tr></table>

<table><tr><td>Data availability</td><td>58</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Supplement</td>
      <td>58</td>
    </tr>
    <tr>
      <td>Acknowledgements</td>
      <td>58</td>
    </tr>
    <tr>
      <td>CHAPTER 3: Morphometrics and machine learning discrimination of the middle Eocene radiolarian species <i>Podocyrtis chalara</i>, <i>Podocyrtis goetheana</i> and their morphological intermediates</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Author contributions</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Abstract</td>
      <td>62</td>
    </tr>
    <tr>
      <td>3.1 Introduction</td>
      <td>63</td>
    </tr>
    <tr>
      <td>3.2 Analyzed morphological groups</td>
      <td>66</td>
    </tr>
    <tr>
      <td>3.3 Materials and methods</td>
      <td>72</td>
    </tr>
    <tr>
      <td>3.3.1 Sediment samples</td>
      <td>72</td>
    </tr>
    <tr>
      <td>3.3.2 Slide preparation</td>
      <td>72</td>
    </tr>
    <tr>
      <td>3.3.3 Microscopy and image processing</td>
      <td>73</td>
    </tr>
    <tr>
      <td>3.3.4 Morphometric analyses</td>
      <td>73</td>
    </tr>
    <tr>
      <td>3.3.5 Artificial Neural Networks</td>
      <td>76</td>
    </tr>
    <tr>
      <td>3.4 Results</td>
      <td>81</td>
    </tr>
    <tr>
      <td>3.4.1 Morphometrics and linear discriminant analysis</td>
      <td>81</td>
    </tr>
    <tr>
      <td>3.4.2 Artificial neural networks</td>
      <td>83</td>
    </tr>
    <tr>
      <td>3.4.2.1 Classification using the four morphogroups</td>
      <td>83</td>
    </tr>
    <tr>
      <td>3.4.2.2 Classification using the three morphogroups supported by LDA</td>
      <td>85</td>
    </tr>
    <tr>
      <td>3.5 Discussion</td>
      <td>88</td>
    </tr>
    <tr>
      <td>3.6 Conclusion</td>
      <td>91</td>
    </tr>
    <tr>
      <td>Data availability</td>
      <td>93</td>
    </tr>
    <tr>
      <td>Acknowledgments</td>
      <td>93</td>
    </tr>
    <tr>
      <td>PART 2: ARTIFICIAL INTELLIGENCE APPLIED TO ASSEMBLAGES</td>
      <td>94</td>
    </tr>
    <tr>
      <td>CHAPTER 4: Subchapter 1 - Convolutional neural network application on a new middle Eocene radiolarian dataset</td>
      <td>94</td>
    </tr>
    <tr>
      <td>Author contributions</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Abstract</td>
      <td>95</td>
    </tr>
    <tr>
      <td>4.1 Introduction</td>
      <td>96</td>
    </tr>
    <tr>
      <td>4.2 Materials and methods</td>
      <td>98</td>
    </tr>
    <tr>
      <td>4.2.1 Core setting and sample preparation</td>
      <td>98</td>
    </tr>
    <tr>
      <td>4.2.2 Image collection and processing</td>
      <td>100</td>
    </tr>
    <tr>
      <td>4.2.3 Taxa selection and dataset</td>
      <td>102</td>
    </tr>
    <tr>
      <td>4.2.4 CNN training</td>
      <td>107</td>
    </tr>
  </tbody>
</table>
<table><tr><td>4.2.5 Performance metrics</td><td>107</td></tr><tr><td>4.2.6. Test set to validate the CNN</td><td>108</td></tr><tr><td>4.2.7 Application on 39 species</td><td>109</td></tr><tr><td>4.3 Results</td><td>109</td></tr><tr><td>4.3.1 Training of the initial dataset</td><td>109</td></tr><tr><td>4.3.2 Performance validation from the test set</td><td>113</td></tr><tr><td>4.3.3 Application on new samples</td><td>114</td></tr><tr><td>4.4 Discussion</td><td>115</td></tr><tr><td>4.4.1 Classification</td><td>115</td></tr><tr><td>4.4.2 CNN training and new test set score</td><td>117</td></tr><tr><td>4.4.3 Feedback on individual species</td><td>117</td></tr><tr><td>4.5 Conclusions</td><td>125</td></tr><tr><td>Data availability</td><td>127</td></tr><tr><td>Acknowledgments</td><td>127</td></tr><tr><td>Appendix A. Supplementary data</td><td>127</td></tr></table>

## CHAPTER 4: Subchapter 2 - Initial middle Eocene radiolarians dataset from the tropical Atlantic (ODP Leg 207) partly classified by K-means clustering

<table><tr><td>4.1 (2) Introduction</td><td>128</td></tr><tr><td>4.2 (2) Results</td><td>129</td></tr><tr><td>4.3 (2) Discussion</td><td>129</td></tr><tr><td>Appendix A: Initial list of all the classes for the radiolarian image database from the Demerara Rise at the Tropical Atlantic Ocean</td><td>132</td></tr></table>

## CHAPTER 5: Using convolutional neural network for improving the biostratigraphy on middle Eocene radiolaria from ODP Leg 207, Site 1260

<table><tr><td>Abstract</td><td>136</td></tr><tr><td>5.1 Introduction</td><td>137</td></tr><tr><td>5.2 Materials and Methods</td><td>138</td></tr><tr><td>5.2.1 Materials</td><td>138</td></tr><tr><td>5.2.2 Methods</td><td>139</td></tr><tr><td>5.3 Results</td><td>140</td></tr><tr><td>5.3.1 CNN prediction and revision</td><td>140</td></tr><tr><td>5.3.2 Correlation with earlier biostratigraphic work</td><td>143</td></tr><tr><td>5.3.3 Biomarkers for the radiolarian zonation</td><td>145</td></tr><tr><td>5.3.4 Remaining bio events</td><td>147</td></tr><tr><td>5.4 Discussion</td><td>150</td></tr></table>
<table><tr><td>5.4.1 General discussion</td><td>150</td></tr><tr><td>5.4.2 Individual species revision and their credibility in future biostratigraphic work</td><td>150</td></tr><tr><td>5.4.3 Time and Effort</td><td>155</td></tr><tr><td>5.5 Conclusion</td><td>155</td></tr><tr><td>Acknowledgments</td><td>156</td></tr><tr><td><b>CHAPTER 6: General Conclusions and Perspectives</b></td><td><b>157</b></td></tr><tr><td>6.1 General conclusions</td><td>157</td></tr><tr><td>6.2 Perspectives</td><td>161</td></tr><tr><td><b>References</b></td><td><b>165</b></td></tr><tr><td><b>Appendices</b></td><td><b>184</b></td></tr><tr><td>Abstract</td><td>185</td></tr><tr><td>Résumé</td><td>278</td></tr></table>
Acknowledgments

I would first like to thank the Marie Curie Skłodowska grant and the PEARL program for giving
support, not just in form of salary and scholarship, but also providing help when it comes to
anything from problem with the accommodation and other administrative things, also for
providing the absolute best programs and encouraging us about options and how to proceed in
one's career both inside and outside of academia. Also, a huge thanks for the other international
students of the PEARL program, that have been a huge help from handling any kind of problems
or situations normally faced for expats, especially when not being fluent in french.

Thanks to my supervisors Taniel Danelian, Pierre Boulet and Philippe Devienne for guiding
me throughout the project, and thanks to both the Evo-Eco-Paléo lab, the CRISTAL lab,
especially the neuromorphic group at IRCICA, for their regular meetings. Thanks also to my
other co-authors present at the same or other labs or institutions around the world. A huge
thanks also to the master students, Aurelien Laforge and Jose Francisco Pinto Cabrera, for their
excellent contribution to this work.

A massive thanks to Johan Renaudie that was able to host me at Museum für Naturkunde in
Berlin and to Allison Hsiang for hosting me at Stockholm University, for a certain period of
time during this PhD.

I would like to give special thanks to my family and my husband, who has always been there
for me from a distance, giving me support, energy and motivation to keep on going and to my
family's close friends in Lille that has helped and supported me no matter the circumstances.

And at last, thank you Ernst for everything, you are forever missed and my biggest inspiration.
# CHAPTER 1: Introduction

## 1.1 The essence of this research

Paleontological data, i.e., fossil remains of dead organisms, can give important understandings on changes in paleodiversity throughout geologic time. The study of past environments and climates and the biotic response to these events are studied by paleontological data, especially with planktonic microfossils. Plankton are small single celled microorganisms living in aquatic environments. They are easy to study due to their small size, high abundance and rapid evolution and can be studied by just extracting them from very small samples. This also enables them to be easily used for biostratigraphy i.e., dating of sediments based on species occurrences.

For being able to make any type of interpretations on diversity changes, biostratigraphy, or other abundance data studies, a huge amount of paleontological data must be collected and analysed which is rather tedious, exhausting and time consuming, but yet a very important work.

The aim of this study is both simplifying the work of fellow taxonomic experts in radiolarian taxonomy by tackling unagreed taxonomic challenges and keep a consistency. It also aims to simplify or automatize the work, so even non-taxonomic experts can use AI for automatic image recognition. This type of research could enhance directly or indirectly the scientific field by automatizing or simplifying the research about radiolarians, their important role with respect to dating sediments used for biostratigraphy in areas where the better studied calcareous nannofossils or foraminifera are not enough. Also, it would be useful for paleoclimatic or paleoenvironmental studies, as well as to study the general response of plankton to long- or short-term climatic events. In industry, this research is very useful for enhancing or partly replacing the work made by biostratigraphers in oil and gas exploration, who use microfossils
to date sediments and to recognize different source rocks, which is a vital part of finding oil and gas. Here we focus on radiolarians coming from the middle Eocene time.

### 1.1.1 The middle Eocene

The Eocene is a time period stretching as far back as 56-33.9 Million years ago (Ma). At the beginning of this period, both Australia and Antarctica were still connected. Also, more major tectonic events occurred during this interval, such as the disappearance of the Tethys Ocean, leading to the formation of the Alpes following the collision of India with Asia. The Eocene is also characterized by significant changes in long-term climate change, with temperatures reaching the highest levels of the Cenozoic at the beginning of the period, which is known as the Paleocene-Eocene Thermal Maximum (PETM). After this peak, the overall long-term temperature followed a global cooling trend, which continued throughout the entire Cenozoic until present day. The middle Eocene, which is the interval of radiolarians studied in this work, is characterized by a long-term cooling, and a single warming event, the Middle Eocene Climatic Optimum (MECO).

### 1.1.2 What are radiolaria?

Radiolaria are a group of single-celled eukaryotes, Protozoa, living in aquatic environments; they are found in all oceans and at all ranges of depths, from the surface to the deep sea. They are very diverse today and throughout geologic time and are very important components of the marine ecosystem, serving as prey for larger animals such as filter-feeding invertebrates; they feed on a variety of smaller organisms, like phytoplankton (Anderson, 1983; Boltovskoy et al., 2017; and Matsuoka, 2007). They are important players in both the carbon (Lampitt et al., 2009)
and silica cycles (Takahashi, 1987). Radiolaria are closely related to foraminifera (Cavalier-Smith et al., 2018) and they both belong to the eukaryotic supergroup Rhizaria.

The radiolaria that are of most interest to paleontology are Polycystines, both because they have been abundant throughout geological time, but also since their test are made of opaline silica which is rather well preserved in the fossil record. They have generally a spherical or cylindrical shape and complex skeletons, which they use for support and protection. Two of the Polycystines orders are Spumellaria and Nassellaria (Fig. 1.1).

Spumellarians are spherical or ovoid with a radial symmetry and bear a complex, often spiny, external skeleton and one or several inner shells. Spumellarians have existed since the Ordovician, approximately 475 Ma ago. Living Spumellaria have been found to live in symbiosis with several different photosynthetic algae, for instance, dinoflagellates, cyanobacteria, prasinophytes, or haptophytes. This symbiotic association is likely the reason of the higher concentrations and greater diversity of Spumellaria in the tropics (Sandin, 2019).

Nassellarians, unlike Spumellarians, have existed since the Carboniferous, approximately 360-300 Ma. The skeleton of Nassellarians is typically cone- to cylindrical-shaped and it has a bilateral symmetry. It is assumed that species diversity in Nassellarians has been increasing since the beginning of the Cenozoic, approximately 66 Ma (Anderson, 1983).
<figure><img src="image_3.png" /><figcaption>**Figure 1.1.** Typical sketches of Spumellarian and Nassellarian skeletons including names for the different skeletal parts. Slightly adjusted from Anderson, (1983).</figcaption></figure>

1.1.3 From living organisms to several million-year-old preserved fossils

Once radiolaria die, only their siliceous skeleton remains and sinks to the bottom of the ocean and accumulates slowly into the sediment and will eventually through time undergo diagenesis; thus, the sediment in the radiolarian skeleton remains will undergo pressure and chemical changes and will lithify (become rock). Biological imprints will eventually get fossilized and become excellent sources for reconstructing past environments (De Wever et al., 2002). Polycystine radiolaria are well preserved throughout the fossil record and are unique among all microfossil groups by having a complete fossil record with a detailed evolutionary history (Sanfilippo and Riedel, 1990).
### 1.1.4 Taxonomic importance of radiolaria

Generally, the classification of extinct organisms is different from biology. In paleontology, classification is based on morphological features. Whereas, in biology, classification on a species level is based on the possibility of producing fertile offspring.

Several hundreds or thousands of extinct or extant radiolaria species have been recognized. Many of these are still not discovered or described by the literature. Generally speaking, the radiolarian taxonomy is still not fully understood due to difficulties in observing the internal parts of the skeleton in radiolarians. Many radiolarian species are hard to place in higher ranks such as genera or families, and many species can be placed into many different families. This is mainly because there are not enough phylogenetic traces for species in each genus (Suzuki and Aita., 2010). Therefore, the classification of radiolaria based on a species level is very important. Haeckel was one of the first people trying to organize and classify radiolarians into higher taxonomic ranks, and there have been several other more or less failed attempts to group radiolarians (De Wever et al., 2001).

Lately, there have been attempts to re-classify certain species into other genera or families by using and connecting molecular studies to extinct species based on extant taxa (Suzuki et al., 2021; Sandin, 2019) but still upon today the Haeckel taxonomy is still dominating within radiolaria research, especially for Cenozoic radiolaria (Lazarus, 2005).

### 1.1.5 Radiolaria used in biostratigraphy

Many microscopic unicellular organisms with fossilized skeleton parts are thanks to their fast evolution, high abundance, and small size extremely useful to biostratigraphy. Fossils themselves cannot be dated using radiometric dating techniques. There are however other
proxies that can be correlated with fossil dating. For example, paleomagnetism is based on the
knowledge that the magnetic north and south poles are constantly moving creating changes in
polarity and by studying the alignment of the magnetic mineral crystallization in for example
magnetite or iron, changes over time can be recorded. This is correlated with well-dated
reference data. Tephrochronology is another age correlation technique. The tephra layers
consisting of volcanic ash deposits in oceanic sediments can be used to correlate age and
compare it with known ages for volcanic eruptions, which have been well-dated.

Originally, the most studied microfossil groups, which are widely used for biostratigraphy,
including for oil and gas exploration, are foraminifera, nannofossils, and palynomorphs.
Cenozoic biozonations based on calcareous nannoplankton and planktonic foraminifera, have
approximate ranges for 2 million years (Bolli et al., 1985; Armstrong and Brasier, 2005).

It was earlier believed that radiolarian species, initially described by Haeckel, were more long-
ranging and could therefore not be useful within biostratigraphy (Campbell, 1984; Lazarus,
2005). However, this was not the case and radiolarians evolved at a similar rate as other
microorganisms and radiolarians are therefore useful in areas lacking or having too poorly
preserved carbonate microfossils (Lazarus, 2005).

In 1985, Sanfilippo et al. (in Bolli et al., 1985) conducted an important work in reviewing 29
existing radiolarian biozones correlated with magnetostratigraphy (Armstrong and Braiser,
2005).

Sanfilippo and Nigrini (1998) revised many of the tropical Eocene biozonations used today in
biostratigraphy. Many of the *Podocyrtis* species are used as biomarkers for the different
radiolarian zonations along with a few other Eocene radiolarian species. The genus *Podocyrtis*
is generally well studied, and its different species occur during the middle Eocene. The first
time this genus was described was by Ehrenberg in 1847. Sanfilippo and Riedel conducted a
morphometric study of the evolution of the *Podocyrtis* species in 1990, showing the *Podocyrtis*
species evolving during the middle Eocene time frame.

The radiolarian biozonations have been calibrated by geomagnetic polarity timescales
(Kamikuri et al., 2012; Souza et al., 2017, 2021; Hollis et al., 2020). The important biozonations
covering our studied materials are from:

RP16 – *Podocyrtis (Lampterium) goetheana* interval zone (Moore, 1971, emend. Riedel and Sanfilippo, 1978). Its base is recognized by the First Occurrence (FO) of *Podocyrtis (Lampterium) goetheana* and the top is identified by the FO of *Cryptocarpium azyx* (Sanfilippo and Riedel).

RP15 – *Podocyrtis (Lampterium) chalara* lineage zone (Riedel and Sanfilippo, 1970, 1978).
The base is distinguished by the Evolutionary Transition (ET) of *Podocyrtis (Lampterium) mitra* Ehrenberg to *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo.

RP14 – *Podocyrtis (Lampterium) mitra* lineage zone (Riedel and Sanfilippo, 1970, 1978). The base is recognized by the ET of *Podocyrtis (Lampterium) sinuosa* Ehrenberg, to *Podocyrtis (Lampterium) mitra* Ehrenberg.

RP13 – *Podocyrtis (Podocyrtoges) ampla* lineage zone (Riedel and Sanfilippo, 1970, 1978).
The base is set by the ET of *Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel, to
*Podocyrtis (Podocyrtoges) ampla* Ehrenberg.
RP12 – *Thyrsocyrtis (Pentalocorys) triacantha* lineage zone (Riedel and Sanfilippo, 1970; emend. Riedel and Sanfilippo, 1978). Which the base is based on the FO of *Eusyringium lagena* (Ehrenberg).

A biostratigraphic research based on radiolarians (Meunier and Danelian, 2022) was recently conducted from the samples used in this work which were correlated with magnetostratigraphy and cyclostratigraphy from site 1260 (Westerhold and Röhl, 2013), resulting in a highly accurate and high resolutional ages for up to 71 different radiolarian bioevents.

### 1.1.6 Geologic setting of ODP Leg 207

The location of the studied radiolarian samples is from the Demerara Rise, a plateau, off the Atlantic coast of Suriname and French Guyana in South America (Fig. 1.2 and 1.3). Sediment samples were collected there from the Ocean Drilling Program (ODP) expedition Leg 207 in 2004 with the aim to recover extended shallowly submerged Cretaceous and Paleogene sediments that could be used for palaeoceanographic studies of the tropical Atlantic Ocean. It was previously known that during this period there were a lot of events happening like periods of ocean anoxia, fast changes in the climate, mass extinction events, and the opening of the Equatorial Atlantic Gateway (Shipboard Scientific Party, 2004). When the sediment samples had been recovered it was discovered that very well-preserved and completed sequences of radiolaria and other siliceous micro remains exist, indicating silica-enriched waters. Hence, the radiolaria diversity and abundance at the Demerara Rise was particularly huge, especially during the middle Eocene. The sedimentation rate during the middle Eocene was also very high, which made it possible for high-resolution studies from this sequence. Due to these facts, this sequence is a great choice for studying Middle Eocene radiolarians.
<figure><img src="image_4.png" /><figcaption>**Figure 1.2.** Paleogeographic World map from 40 Ma. Constructed by https://www.odsn.de/odsn/services/paleomap/paleomap.html#Form (Hay et al., 1999).</figcaption></figure>

<table><caption>**Table 1.1.** Samples from ODP Leg 207 from the Demerara rise and their exact coordinates, water depths and sedimentation depths.</caption><thead><tr><td>Leg/Hole</td><td>Coordinates</td><td>Water depth (mbsl)</td><td>Sediment depth (mbsf)</td></tr></thead><tbody><tr><td>207-1258A</td><td>9°26.00030′N, 54°43.9994′W</td><td>3192 mbsl</td><td>8-253 mbsf</td></tr><tr><td>207-1259A</td><td>9°17.99890′N, 54°11.9984′W</td><td>2354 mbsl</td><td>125-445 mbsf</td></tr><tr><td>207-1260A</td><td>9°15.94850′N, 54°32.6327′W</td><td>2549 mbsl</td><td>35-335 mbsf</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Figure 1.3.** Sample location (Shipboard Scientific Party, 2004)</figcaption></figure>

At this time, the location of the Demerara Rise, located in the northeastern part of the South American continent, was much closer to the African continent than it is today. North and South America were also not connected, leading to potential influences of Pacific Ocean waters, and the Tethys Sea north of Africa.

### 1.1.7 The importance of Radiolaria for paleoenvironments and paleoceanography

Throughout geologic time, there have been major changes in the climate, with drastic and more long-term changes in temperatures. Understanding the biotic response to these events could help in understanding the ecological and biological effects of the drastic global warming that the Earth is facing today. Understanding the response in microorganisms, which are the primary producers and first-order consumers, is crucial. As of today, the response of radiolaria to global
change is not well known, especially their response to diversity or abundance. As mentioned in the taxonomic part, there are a lot of difficulties dealing with taxonomy and we are far from all species being described in the literature, making it difficult to work on reliable diversity or correct abundance data.

This reflects the current situation of our samples, as we are dealing with tropical Atlantic radiolarian assemblages and the middle Eocene radiolarian diversity is huge and based on our estimates it accounts for ca. 500 species, many of which are not described yet.

### 1.1.8 Machine learning techniques and Artificial Intelligence

There are many different morphometric tools and methods that are frequently used in paleontology to discriminate closely related or similar-looking species or morphotypes based on morphological criteria. One supervised method to find similar data points that can be used to discriminate morphospecies among labeled groups is Linear Discrimination Analysis (LDA). By using a set of different measured characteristics for labeled individuals, LDA can use this information to reduce the dimensions and find the common denominator for each group.

K-Nearest Neighbour (KNN) is another supervised machine learning technique that works by only feeding in images and letting the algorithm find common data points (pixel values) within the different groups.

During the last couple of years, Artificial Intelligence (AI) has become more advanced and developed in a way that can improve these problematic classification tasks. Convolutional Neural Networks (CNNs) are a class within AI, that are popular to use for analysis and classification of images. The function behind CNNs is that it recognizes patterns in images. The CNNs are simply constructed by input layers, which transfer their information into hidden
layers, which are the convolutional layers that will process and transform the information into the next layers. Each convolutional layer has different sizes, and numbers of filters. A filter can be seen as a small grid of pixels with different pixel values in each grid corresponding to a specific colour value. This grid will go through the entire image in a sliding (convolving) way and transform the new values to the next layer that will process the image in a different or similar way. Early layers could for example easily detect edges, circles, or corners, and later layers can even recognize more specific objects.

Spiking Neural Networks (SNNs) is a third-generation neural network and is constructed in a way that closely imitates biological brains. In such networks, information is encoded as streams of time events, the spikes, and processes these spike streams in an asynchronous event-based way instead of in a fixed clock cycle. A SNN uses analog neurons, which in comparison with CNN do not fire at each propagation cycle. A neuron fires (produces a spike) when it has reached a certain value, a “threshold”. It takes time for neurons to fire and to return to a stable state after being fired. The time after a neuron has spiked until it has returned to its stable state is called the “refractory period”, meaning that during this time, the neuron does not respond to any input activity. Learning in SNNs can be supervised (requiring a labeled training dataset) or unsupervised (allowing automatic clustering of the samples of the training dataset). Another strength of SNNs is their ultra-low power consumption when implemented in hardware, opening the way in the future to autonomous automatic classifying microscopes. On the negative side, they are not as well understood as other Artificial Neural Networks (ANNs) and their classification performance lags behind that of ANNs.
1.1.9 Earlier research using AI in image recognition in micropaleontology

There have been several studies trying to apply automatic image recognition using AI or more
specifically CNNs. Already in 1996, the first automatic image recognition was used for
coccolithophores. The SYstème de Reconnaissance Automatique de Coccolithes (SYRACO),
version 1 was presented at a conference, obtaining an average classification accuracy of 49 %.
In 1999, a second version of SYRACO was published in a paper by Dollfus and Beaufort, using
a simple four-layered deep neural network, which obtained a mean accuracy of about 86%. It
wasn't until two years later that the SYRACO was implemented, for the first time, into a real
scientific approach for reconstructing Pleistocene oceanic primary productivity (Beaufort et al.,
2001). Throughout the years, SYRACO has continued to be developed and so has a lot of other
AI studies for automatic image recognition of other microfossil groups.

This thesis has been inspired by some pioneering earlier works that used automatic image
classification of radiolarians, such as the studies conducted by Renaudie et al. (2018) and Tetard
et al. (2020). Renaudie et al. (2018) applied the MobileNet CNN architecture for the automatic
radiolarian classification of 16 closely related species of the Cenozoic genera *Antarctissa* and
*Cycladophora*. This work resulted in a classification accuracy of about 73 %, which was
increased to ca. 90 % after ignoring unclassified specimens. Tetard et al (2020) described a new
workflow for radiolarian image acquisition, including sediment preparation from the removal
of non-siliceous particles, to slide settlements on small 1.2 x 1.2 cm large coverslips on
decanters built with a 3D printer to help with a random and uniform settling of particles and
also a 3D printed coverslip guide, to ease the application of several small coverslips into one
single glass slide. This work also trained a ResNet50 architecture CNN on 132 classes of
Miocene to recent radiolarians and obtained an accuracy of about 90 %, and all work was done
with the software ParticleTrieur.

Several other important studies are using automatic image recognition for fossil identification.

A few of them are on foraminifera (ex. Mitra et al., 2019, Hsiang et al., 2019; Carvalho et al., 2020; De Lima et al., 2020; Marchant et al., 2020), pollen (ex. Goncalves et al., 2016; Bourel et al., 2020) and even some other radiolarian studies (ex. Itaki et al., 2020).

**1.2 Thesis objectives and structure**

The general objective of this thesis is to apply Artificial Intelligence for automatic image
recognition of tropical Atlantic middle Eocene radiolarian taxa.

The main scientific questions that we will try to address in this thesis are as follows:

Q.1 How can neural network learning achieve equal accuracy in the identification of middle Eocene radiolarian species as an Eocene specialist in radiolarian taxonomy?

Q.2 How can machine learning techniques be compared to classical morphometric analyses?

Q.3 How can Spiking Neural Networks (SNNs) be compared with other deep learning methods?

Q.4 Can a trained neural network on a whole middle Eocene tropical radiolarian assemblage be directly applied for example to biostratigraphic or palaeoceanographic studies?

In order to address these questions two distinct approaches were followed; for chapters 2 and 3 (Part 1) the images used were taken on individually picked specimens, while for chapters 4 and
5 (Part 2), the image dataset used was obtained after screening the entire radiolarian assemblage
with the use of an automated microscope.

In Chapter 2, we tackle the first scientific question (Q.1) of this thesis; focused on eight closely related species belonging to the genus *Podocyrtis*.

The first step here was to train a labelled convolutional neural network, in which we had to sample a lot of images for each species, for which specimens were picked up individually; we thus formed a well selected database of images, which were later trained on a MobileNet v1 CNN architecture and obtained a high training accuracy for automatic image recognition. At a later stage, we wanted to test the trained CNN, to see if the trained automatic image classification works in a real case study. For this test, we obtained more radiolarian images from other parts of the ocean of the same genus and let the CNN decide the label. This was later checked on, and in many cases, the CNN could identify the correct specimen or at least classify it to a neighboring species along a lineage.

Chapter 3 represents a follow-up study on *Podocyrtis* species, where we attempt to address the presence of intermediate forms between species *P. chalara* and *P. goetheana*, but also the two last scientific questions (Q.2 and Q.3). We tried to use supervised morphometrical analyses, such as Linear Discrimination Analyses (LDA), to investigate if each individual form shares or has their morphospace. We also used the morphospecies along with intermediate forms to train in ANNs. Except for using CNNs, we also introduced the use of SNNs. Both AI and morphometrics showed that the possible intermediate form resembling to *P. chalara* is in fact only recognized as *P. chalara*; on the contrary, the intermediate form closer to *P. goetheana* can be presented as a new intermediate morphospecies, since it can be clearly separated both
by the morphological disparity analysis and by the AI. From the different types of AI analyses,
we could conclude that using a SNN is faster and is known to be of less energy consumption
than using a CNN.

In both Chapters 4 and 5, we deal with the last scientific question (Q.4). In Chapter 4 we wanted to deal with the whole middle Eocene radiolarian assemblages but focused only on 39 important Nassellarian species. Here, we trained all objects and radiolarians appearing, preferably at the species level; however, this was not always possible and therefore higher taxonomic ranks were also included. We obtained a high training accuracy and applied this trained CNN to new samples, in which we wanted to focus on finding a possible application that could be easily obtainable with this specific trained neural network.

In Chapter 5, we focus only on the automatic image application that is solely based on the
trained CNN of Chapter 4. Here we applied biostratigraphy, only focusing on the
biostratigraphically important species.
# PART 1: ARTIFICIAL INTELLIGENCE APPLIED TO INDIVIDUAL SPECIES

## CHAPTER 2: Artificial intelligence applied to the classification of eight middle Eocene species of the genus *Podocyrtis* (Polycystine radiolaria)

Veronica Carlsson<fnref n="1" />,<fnref n="2" />*, Taniel Danelian<fnref n="1" />, Pierre Boulet<fnref n="2" />, Philippe Devienne<fnref n="2" />, Aurelien Laforge<fnref n="1" />,<fnref n="2" /> and Johan Renaudie<fnref n="3" />

<footnote n="1">¹Univ. Lille, CNRS, UMR 8198, Evo-Eco-Paleo, F-59000 Lille, France.</footnote>

<footnote n="2">²Univ. Lille, CNRS, CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, UMR 9189, F-59000 Lille, France.</footnote>

<footnote n="3">³Museum für Naturkunde, Leibniz-Institut für Evolutions- und Biodiversitätsforschung, 10115 Berlin, Germany</footnote>

<footnote>* Corresponding author. E-mail address: [veronica.carlsson@univ-lille.fr](mailto:veronica.carlsson@univ-lille.fr)</footnote>
## Author contributions

Veronica Carlsson is the main author and writer of this paper. She formulated the research methodology in discussion with some of the other authors. Additionally, she classified, analyzed, collected, and processed all images and built up the datasets.

Taniel Danelian contributed to the discussion of formulating the research methodology and confirmed the classifications made by the main author. He also provided supervision, guidance, and reviewed the paper.

Pierre Boulet was involved in the discussion of formulating the research methodology and contributed to supervision, guidance, and reviewed the paper.

Philippe Devienne also participated in the discussion of formulating the research methodology and provided supervision, guidance, and reviewed the paper.

Aurelien Laforge contributed to this paper by coding the neural networks and the rotations of the images.

Johan Renaudie contributed to the discussion and reviewed the paper.

## Abstract

This study evaluates the application of artificial intelligence (AI) to the automatic classification of radiolarians and uses as an example eight distinct morphospecies of the Eocene radiolarian genus *Podocyrtis*, which are part of three different evolutionary lineages and are useful in biostratigraphy. The samples used in this study were recovered from the equatorial Atlantic (ODP Leg 207) and were supplemented with some samples coming from the North Atlantic and Indian Oceans. To create an automatic classification tool, numerous images of the
investigated species were needed to train a MobileNet convolutional neural network entirely coded in Python. Three different datasets were obtained. The first one consists of a mixture of broken and complete specimens, some of which sometimes appear blurry. The second and third datasets were leveled down into two further steps, which excludes broken and blurry specimens while increasing the quality. The convolutional neural network randomly selected 85 % of all specimens for training, while the remaining 15 % were used for validation. The MobileNet architecture had an overall accuracy of about 91 % for all datasets. Three predicational models were thereafter created, which had been trained on each dataset and worked well for classification of *Podocyrtis* coming from the Indian Ocean (Madingley Rise, ODP Leg 115, Hole 711A) and the western North Atlantic Ocean (New Jersey slope, DSDP Leg 95, Hole 612 and Blake Nose, ODP Leg 171B, Hole 1051A). These samples also provided clearer images since they were mounted with Canada balsam rather than Norland epoxy. In spite of some morphological differences encountered in different parts of the world's oceans and differences in image quality, most species could be correctly classified or at least classified with a neighboring species along a lineage. Classification improved slightly for some species by cropping and/or removing background particles of images which did not segment properly in the image processing. However, depending on cropping or background removal, the best result came from the predictive model trained on the normal stacked dataset consisting of a mixture of broken and complete specimens.

## 2.1 Introduction

Polycystine radiolarians belong to an extant group of marine zooplankton protists secreting an aesthetically pleasing siliceous test that is rather well preserved in the fossil record and is therefore of importance to both biostratigraphy and paleoceanography. They are unique
amongst skeleton-bearing planktonic representatives in having a fossil record stretching as far back as the early Cambrian (Obut and Iwata, 2000; Pouille et al., 2011; Aitchison et al., 2017). Their continuous Cenozoic fossil record has allowed description of a number of well-documented evolutionary lineages (Sanfilippo and Riedel, 1990), although their taxonomy has still not been fully clarified in spite of the great progress achieved during the last few decades (O'Dogherty et al., 2021). Polycystine radiolarian classification at the species level is based on morphological criteria, which therefore bear particular significance if one wishes to address evolutionary questions, but also for the development of high-resolution biostratigraphy. Supervised learning uses labeled data to train algorithms that will enable automatic classification and computer vision to deal with information from a visual context such as digital images or videos. These are some of the branches of artificial intelligence (AI) that have been developed during the last few years and may provide solutions to a number of difficult classification tasks. As such, convolutional neural networks (CNNs) use a deep learning algorithm to recognize patterns in images in a grid-like arrangement with multiple layers (Hijazi et al., 2015), which is a common approach for the analysis and classification of images. Training CNNs in a supervised way requires both a labeled training and validation dataset, from which the CNNs will learn features and patterns unique to each class from the training set by forming outputs, with which the untrained validation data will respond to if the model is a good fit.

A number of studies have attempted to apply automatic classification techniques on microfossils and/or micro-remains by using supervised machine learning in the past. Dollfus and Beaufort (1999) were the first micropaleontologists to apply AI in classifying and counting coccolithophores. They created the software SYRACO as an automatic recognition system of coccoliths, which was further developed a few years later (Beaufort and Dollfus, 2004) to count
automatically identified coccoliths but also for application to late Pleistocene reconstructions
of oceanic primary productivity (Beaufort et al., 2001). Goncalves et al. (2016) tried to
automatically classify modern pollen from the Brazilian savannah using different algorithms
and achieved a highest median accuracy of 66 %, which is nearly as high as the median accuracy
obtained by humans, based on a dataset that consisted of 805 specimens and 23 classes of pollen
types. Hsiang et al. (2019) trained a neural network of 34 different modern species of
foraminifera using a large dataset of a few thousand images, which reached an accuracy
exceeding 87.4 %. Carvalho et al. (2020) used 3D images of 14 species of foraminifera,
obtaining a dataset as large as 4600 specimens and a microfossil identification and segmentation
accuracy as high as 98 % by using the CNN architectures of Resnet34 and Resnet50 with
adjustment of hyperparameter optimization. De Lima et al. (2020) used a relatively small
dataset of fusulinids composed of 342 images (including training, validation and test sets),
which were divided into eight classes on a genus level to train in different CNNs architectures.
They obtained the highest accuracy of 89 % by using the finetuning InceptionV3 model.
Marchant et al. (2020) managed to train a CNN on a very large dataset of 13 001 images,
including 35 different species of foraminifera. The best accuracy they obtained was about 90
%.
Tetard et al. (2020) developed an automated method for new slide preparations, image
capturing, acquisition and identification of radiolarians with the help of a software known as
ParticleTrieur. They attempted to classify all common radiolarians existing since the Miocene,
in a total of 132 classes, with 100 of them being relatively common species. They obtained an
overall accuracy of about 90 %.
Itaki et al. (2020) developed an automatization for the
acquisition and deep learning of a single radiolarian species, *Cycladophora davisiana*, and
obtained an accuracy similar to a human expert. Interestingly, they managed to be three times
faster than a human being. Finally, Renaudie et al. (2018) applied the computationally efficient MobileNet convolutional neural network architecture for automatic radiolarian classification of 16 closely related species of the Cenozoic genera *Antarctissa* and *Cycladophora*. They obtained an overall accuracy of about 73%, which they managed to increase to ca. 90% after ignoring specimens which were not classified at all and by only including those specimens which had been given a class by the CNN.

The objective of our study was to obtain an accurate system of automatic classification for an automated classification of eight closely related species belonging to the middle Eocene genus *Podocyrtis* Ehrenberg, 1846 to be used by non-specialists in radiolarian taxonomy (e.g., students, industrial biostratigraphers or geochemists). Several of these species have a very good fossil record and are important in biostratigraphy as well as in morphometrics and evolutionary studies including gradual evolutionary transitions (Sanfilippo and Riedel, 1990; Danelian and MacLeod, 2019). In this work, we wished to implement MobileNet version 1 (Howard et al., 2017) because of its simplicity and lightweight construction, which enabled us to run more data in a shorter time. Many examples of MobileNet are available online and it is relatively easy to reproduce this work, which could thus be seen as a starting point for any other type of CNN implementation.

We will therefore attempt to answer the following scientific questions:

1. How well can the MobileNet convolutional neural network classify closely related species of the genus *Podocyrtis*?

2. How well can the predictive model classify *Podocyrtis* species under different processing settings and with materials coming from different parts of the world's ocean?
## 2.2 Materials

The main radiolarian material used for this study comes from the South American margin off Surinam (Demerara Rise, Ocean Drilling Program (ODP) Leg 207, Shipboard Scientific Party, 2004, see Table 1.1), where the middle Eocene interval is composed of an expanded sequence of chalk rich in abundant and well-preserved siliceous microfossils (for more details see Danelian et al., 2005, 2007; Renaudie et al., 2010). We focused on eight closely related species of the genus *Podocyrtis* (Fig. 2.1). Taxonomic concepts followed in this study are in accordance with Riedel and Sanfilippo (1970), Sanfilippo et al. (1985), Sanfilippo and Riedel (1990, 1992), with their stratigraphic ranges as specified recently by Meunier and Danelian (2022).

The eight studied species are considered to be part of three distinct evolutionary lineages, classified as three subgenera of the genus *Podocyrtis: Podocyrtis, Podocyrtoges and Lampterium* (Sanfilippo and Riedel, 1992). All taxonomic assignments were performed by a single taxonomist but were also checked by two other experts who had access to all 2D images of the dataset. The *Podocyrtis* species are in general relatively easy to recognize by their outer shape and/or size and distribution of pores (Figs. 2.1 and 2.2).
<figure><img src="image_6.png" /><figcaption>**Figure 2.1.** Age range and evolutionary relationships of *Podocyrtis* species occurring in Hole 1260A modified from Meunier and Danelian (2022). Arrows indicate descending species.</figcaption></figure>
<table><caption>**Table 2.1.** Number of specimens per species for each sample coming from ODP Leg 207 which were used for training and validation.</caption><thead><tr><th colspan="12">ODP<br>Leg 207</th></tr><tr><th>Hole</th><th>Sample</th><th>Radi<br>olari<br>an<br>zone</th><th>P.<br>papalis</th><th>P.<br>goetheana</th><th>P.<br>chalara</th><th>P.<br>mitra</th><th>P.<br>sinuosa</th><th>P.<br>ampla</th><th>P.<br>phyxis</th><th>P.<br>diamesa</th><th>Specimens per<br>sample</th></tr></thead><tbody><tr><td rowspan="7">1259A</td><td>18R-1W, 53-55 cm</td><td>RP1<br>6</td><td>8</td><td>57</td><td></td><td></td><td></td><td></td><td></td><td></td><td>65</td></tr><tr><td>18R-2W, 53-55 cm</td><td></td><td>16</td><td>56</td><td></td><td></td><td></td><td></td><td></td><td></td><td>72</td></tr><tr><td rowspan="3">20R-3W, 53-55 cm</td><td>RP1<br>5</td><td>100</td><td></td><td>130</td><td>2</td><td></td><td></td><td></td><td></td><td>232</td></tr><tr><td>RP1<br>4<br>and<br>21R-CC, 63-177 cm<br>and 25R-CC, 63-177<br>cm (mixed)</td><td>RP1<br>3</td><td>57</td><td></td><td></td><td>90</td><td>1</td><td></td><td></td><td></td><td>148</td></tr><tr><td>26R-3W, 54-56 cm</td><td>RP1<br>2</td><td>15</td><td></td><td></td><td></td><td>25</td><td></td><td>42</td><td></td><td>82</td></tr><tr><td rowspan="14">1260A</td><td>6R-1W, 55-57 cm</td><td>RP1<br>6</td><td>2</td><td>4</td><td></td><td></td><td></td><td></td><td></td><td></td><td>6</td></tr><tr><td>6R-2W, 55-57 cm</td><td></td><td></td><td>18</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td>19</td></tr><tr><td>6R-4W, 55-57 cm</td><td></td><td></td><td></td><td>2</td><td></td><td></td><td></td><td></td><td></td><td>2</td></tr><tr><td>6R-CC, 63-177 cm</td><td>RP1<br>6/15</td><td>5</td><td></td><td>15</td><td></td><td></td><td></td><td></td><td></td><td>20</td></tr><tr><td>7R-6W, 54-56 cm</td><td>RP1<br>5</td><td>4</td><td></td><td></td><td>2</td><td></td><td></td><td></td><td></td><td>6</td></tr><tr><td>7R-CC, 63-177 cm</td><td></td><td></td><td></td><td>5</td><td>1</td><td></td><td></td><td></td><td></td><td>6</td></tr><tr><td>8R-3W, 54-56 cm</td><td></td><td>7</td><td></td><td>20</td><td></td><td></td><td></td><td></td><td></td><td>27</td></tr><tr><td>8R-5W, 54-56 cm</td><td></td><td></td><td></td><td>2</td><td></td><td></td><td></td><td></td><td></td><td>2</td></tr><tr><td>8R-6W, 54-56 cm</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>9R-1W, 55-57 cm</td><td>RP1<br>5/14</td><td>3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3</td></tr><tr><td>9R-2W, 55-57 cm</td><td></td><td>11</td><td></td><td></td><td>14</td><td></td><td></td><td></td><td></td><td>25</td></tr><tr><td>10R-5W, 55-57 cm</td><td>RP1<br>4</td><td>19</td><td></td><td></td><td>82</td><td></td><td>44</td><td></td><td></td><td>145</td></tr><tr><td>13R-1W, 54-55 cm</td><td>RP1<br>4/13</td><td>10</td><td></td><td></td><td></td><td>2</td><td></td><td></td><td></td><td>12</td></tr><tr><td>15R-4W, 55-57 cm</td><td>RP1<br>3/12</td><td>8</td><td></td><td></td><td></td><td>23</td><td>7</td><td></td><td></td><td>38</td></tr><tr><td>16R-1W, 55-57 cm</td><td></td><td>5</td><td></td><td></td><td></td><td>26</td><td>57</td><td>17</td><td></td><td>105</td></tr><tr><td>17R-1W, 55-57 cm</td><td></td><td>4</td><td></td><td></td><td></td><td></td><td></td><td>9</td><td></td><td>13</td></tr><tr><td>17R-CC, 63-177 cm</td><td></td><td>10</td><td></td><td></td><td></td><td>10</td><td></td><td>26</td><td></td><td>46</td></tr><tr><td>19R-6W, 55-57 cm</td><td></td><td></td><td></td><td></td><td></td><td>7</td><td></td><td></td><td></td><td>7</td></tr><tr><td>22R-CC, 63-177 cm</td><td></td><td>3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3</td></tr><tr><td colspan="2">Specime<br>ns per<br>species</td><td></td><td>288</td><td>135</td><td>177</td><td>189</td><td>92</td><td>46</td><td>64</td><td>94</td><td>1084</td></tr></tbody></table>

The *Podocyrtis* subgenus represents an ancestral lineage that experienced morphological stasis.

It is represented by the single morphospecies *Podocyrtis papalis* Ehrenberg, 1847, which differs from all the other *Podocyrtis* species by its partly developed abdomen (often smaller than the
thorax), its overall fusiform shape (largest test width located on its thorax) and weakly expressed lumbar stricture. Three shovel shaped feet are often present, as well as a well-developed apical horn (which may be broken sometimes).

The *Podocyrtoges* subgenus is composed of three distinct morphospecies that belong to a lineage that evolved anagenetically. These are, from oldest to youngest:

*Podocyrtis diamesa* Riedel and Sanfilippo, 1970 differs from *P. papalis* by a more distinct lumbar stricture, with rather equally sized thorax and abdomen, and a more elongated than fusiform body. Some of the stratigraphically late forms of *P. papalis* display a degree of similarity in shape to *P. diamesa* (Fig. 2.2, 13th image), although the latter is much bigger in size (Fig. 2.1) and bears a larger apical horn than *P. papalis*.

*Podocyrtis phyxis* Sanfilippo and Riedel, 1973 displays a very distinct lumbar stricture formed at the junction between the abdomen and the thorax, with the former being more inflated than the latter. The overall outline of the test recalls the number eight “8”. In general, a large horn is present on the cephalis. Complete specimens were rare in our material, as their horn is fragile and often broken.

*Podocyrtis ampla* Ehrenberg, 1874 displays a conical outline and a less prominent lumbar stricture than *P. phyxis*, with its abdomen being widest distally. Stratigraphically late forms of *P. ampla* do not display any feet and these forms were selected for this study.

The *Lampterium* subgenus is composed of four distinct morphospecies that belong to a lineage that also evolved anagenetically. These are, from oldest to youngest:
*Podocyrtis sinuosa* Ehrenberg, 1874 displays a barrel-shaped abdomen that is larger and more inflated than its thorax. Its widest part is located centrally at the mid-height of the abdomen.

*Podocyrtis mitra* Ehrenberg, 1854 displays an abdomen that is widest distally, rather than at mid-height as in *P. sinuosa*. It also displays more than 13 pores in the circumference of the widest part of the abdomen. Specimens with a rough surface on the abdomen, which could possibly be assigned to *P. trachodes* in the sense of Riedel and Sanfilippo, 1970, were included under *P. mitra*.

*Podocyrtis chalara* Riedel and Sanfilippo, 1970 displays a thick-walled abdomen, with large and more regularly arranged pores than *P. mitra*. It differs from the latter by having less than 13 pores in the circumference of the widest part of its abdomen. The *P. chalara* specimens selected for our material display 8 to 10 pores in circumference, so that they could be clearly distinguished from *P. mitra*.

*Podocyrtis goetheana* (Haeckel, 1887) displays long straight bars on its abdomen that enclose exceptionally large pores. The largest of them are located at the middle row of pores. They are often elongated, with four pores in the circumference. There are, however, some noticeably short specimens in our material that clearly belong to *P. goetheana* (Fig. 2.2, 4th image). No feet are present.

A total of 1085 radiolarian specimens were selected from the material available from the Demerara Rise. Their images were taken and prepared at the University of Lille and used for both training and validation of the CNN. The number of specimens used per species varies between 46 and 288 (Table 2.1). A second testing set of samples was prepared with 22 specimens that are stored at the Museum für Naturkunde in Berlin (see Table 2.2). Ten of them
come from the Indian Ocean (Madingley Rise, ODP Leg 115, Hole 711A, Shipboard Scientific Party, 1988), six other specimens come from the western North Atlantic Ocean (New Jersey slope, Deep Sea Drilling Project (DSDP) Leg 95, Hole 612, Shipboard Scientific Party, 1987) and six others also from the western North Atlantic Ocean (Blake Nose, ODP Leg 171B, Hole 1051A, Shipboard Scientific Party, 1998).

<table><caption>**Table 2.2.** Number of specimens per species for each sample coming from ODP Leg 115, DSDP Leg 95 and ODP Leg 171, which were used for test.</caption><thead><tr><th></th><th>Sample</th><th>Radiolarian zone</th><th>Podocyrtis specimens</th></tr></thead><tbody><tr><td colspan="4">ODP Leg 115</td></tr><tr><td rowspan="3">Hole 711A</td><td>20X-2, 137-143 cm</td><td>RP16-RP15</td><td>5</td></tr><tr><td>22X-2, 75-81 cm</td><td>RP15-RP14</td><td>3</td></tr><tr><td>23X-1, 84-90 cm</td><td>≤ RP14</td><td>2</td></tr><tr><td colspan="4">DSDP Leg 95</td></tr><tr><td rowspan="5">Hole 612</td><td>20-1, 70-78 cm</td><td>RP16-RP14</td><td>2</td></tr><tr><td>20-5, 106-116 cm</td><td></td><td>1</td></tr><tr><td>22-6, 41-51 cm</td><td>≤ RP14</td><td>1</td></tr><tr><td>27-1, 46-55 cm</td><td></td><td>1</td></tr><tr><td>33-1, 45-56 cm</td><td></td><td>1</td></tr><tr><td colspan="4">ODP Leg 171</td></tr><tr><td rowspan="4">Hole 1051A</td><td>9H-04, 50-55 cm</td><td>RP16-RP15</td><td>2</td></tr><tr><td>10H-02, 44-50 cm</td><td>≤ RP16</td><td>1</td></tr><tr><td>31X-02, 44-50 cm</td><td>≤ RP14</td><td>1</td></tr><tr><td>38X-02, 50-55 cm</td><td></td><td>2</td></tr><tr><td colspan="3">Total number of specimens</td><td>22</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Figure 2.2.** A selection of the variety of *Podocyrtis* morphotypes analyzed in this study. 1-4) *P. goetheana*. 1-2) from 207_1259A_18R_1W. 53-55 cm; 3) 207_1260A_6R_2W. 55-57 cm; 4) 207_1259A_18R_2W. 53-55 cm. 5-6) *P. chalara* from 207_1259A_20R_3W. 53-55 cm. 7-9) *P. mitra* from 207_1260A_10R_5W. 55-57 cm. 10-12) *P. sinuosa*. 10) from 207_1259A_26R_3W. 54-56 cm; 11) 207_1260A_19R_6W. 55-57 cm; 12) Indian Ocean. 115_711A_25X_1. 83-86 cm. 13-14) *P. papalis*. 13-14) late (13) and typical (14) forms from 207_1259A_20R_3W. 53-55 cm. 15-16) *P. diamesa* from 207_1259A_26R_3W. 54-56 cm. 17-18) *P. phyxis* from 207_1260A_16R_1W. 55-57 cm. 19-20) *P. ampla* from 207_1260A_10R_5W. 55-57 cm. Scale bar is 50 µm.</figcaption></figure>
## 2.3 Methods

We followed two different approaches for collecting photographs of specimens of *Podocyrtis* with the aim of constructing image datasets. A first approach involved the use of radiolarian slides from Leg 207, Hole 1260A, prepared initially for a biostratigraphic examination. The second involved the collection of *Podocyrtis* specimens, picked up directly and individually from dried residues of washed samples coming from both Holes 1260A and 1259A. The challenge faced while taking images of *Podocyrtis* from the old slides consists in specimens often touching themselves or overlapping with other objects. This led as a consequence to individual *Podocyrtis* specimens not being segmented properly by the methods described below and requiring manual (and time-consuming) segmentation.

### 2.3.1 Manual picking of individual *Podocyrtis* specimens

The residues used for sample preparation had already undergone acidic cleaning and removal of other non-siliceous particles by first dissolving the samples in hydrogen peroxide and afterwards in hydrochloric acid, followed by sieving at 50 µm. Some of the samples needed further sieving to remove particles smaller than 45 µm. They were then dried in a 50-60 °C oven.

Specimens of the various species of *Podocyrtis* were manually picked one by one under a ZEISS SteREO Discovery V20 microscope. The radiolarians were then transferred to a 32×24 mm coverslip and placed in such a way so as to avoid them being in contact with each other. A few drops of distilled water were placed on the coverslip for radiolarians to attach to the coverslip. Thereafter, they were dried overnight in an approximately 50 °C oven and thereafter attached on slides with Norland epoxy.
### 2.3.2 Image acquisition and processing

Images of *Podocyrtis* were taken under a Zeiss Axio A2 transmitted light microscope using the Zen 3.2 software with ×100 magnification and a pixel size set to 0.35 µm per pixel. Images were taken in fields of view (FOV), enabling several radiolarians to be captured at once in the same FOV. Approximately 3–15 focal points were taken on each FOV, depending on the specimen size. The images were then stacked using Helicon Focus 7 (Fig. 2.3a–c). They were then segmented (i.e., isolated from the background) all at once (Fig. 2.3c–d) using the ImageJ BioVoxxel plugin (Brocher, 2022) and a modified version of the Autoradio_Segmenter plugin (Tetard et al., 2020).

The images were then further processed with a script from Scikit Image version 0.18.1 (Van der Walt et al., 2014), using Python version 3.7.10 (Van Rossum and Drake, 2009), which rotated them in the same direction along a diagonal angle by finding the longest axis on the radiolarian specimen without cutting off objects within the picture (Fig. 2.3d–h). Thereafter all images were resized to equal 256 × 256 pixels. Having all images in the same orientation decreases the variability and increases the accuracy of the neural network. By having the images rotated in a diagonal angle optimizes the pixel resolution.

The most time-consuming task is the collection of images, as numerous images are needed for each species to build up a consistent dataset. Automatization of this task may be facilitated by the use of an automatic microscope, as in Itaki et al. (2020), Tetard et al. (2020) and Marchant et al. (2020).

The time needed for picking, slide mounting, photographing and image processing of around 100–200 radiolarians was one day. Manual picking speeds up the process, although caution
should be exercised to add glycerin or gelatin instead of water while mounting individually picked radiolarians on coverslips, in order to avoid formation of bubbles.

<figure><img src="image_8.png" /><figcaption>**Figure 2.3.** Image processing. (a) Three “raw” images taken from the microscope from the same FOV but different focal points, which are (b) stacked together using the Helicon Focus 7 software (c) to produce one entire focused and crisp image in which each particle in one FOV is segmented into an individual images or so-called vignette. (d) The segmented images are also transformed into a square 8-bit grayscale with a black background and white objects that are (e) further processed in Python by first rotating the radiolarian objects in the vignettes with a 45° angle so that the longest axis goes from the upper left corner to the lower right corner. (f) The vignettes are then filled again into squares so that no parts of the specimens are removed. (g)</figcaption></figure>
Thereafter, they are cut again into squares just precisely so that the specimen fills the entire square and, lastly the new images
are resized to 256 pixels on each side image (h).

2.3.3 Datasets

The radiolarian specimens included in the training and validation datasets contain only
individuals that display a variability that may be included in the morphological boundaries
accepted in the concept of each one of the eight species. Specimens that could not be identified
with certainty as one or the other morphospecies (i.e., intermediate forms) were removed. All
images are in full focus or so called “stacked”. The images were divided into three different
leveled datasets: the “normal” stacked dataset, called the “S” dataset (Fig. 2.4); the “SC”
dataset, with only complete (unbroken) specimens, which obviously contains fewer radiolarian
specimens but images of good quality; and the “SCUB” dataset, for which all blurry images
were removed from the “SC” dataset (Fig. 2.4).

In all these datasets, 85 % of all specimens were used for training the model and 15 % of all
specimens were only used to validate the trained neural network with the train and test split
function from Scikit-learn (Pedregosa et al., 2011). This distribution aims to keep enough
images to have quality learning, while having enough images for the network assessment to
make sense of and to avoid miscalculations by running the model several times. Since the
training and validation sets were randomized each time, it was important to perform several
runs and thereafter take note of an average accuracy value. Neural network performance was
compared between these different datasets.
<figure><img src="image_9.png" /><figcaption>**Figure 2.4.** Images of fully processed specimens (vignettes) with stacking, segmentation, rotation and resizing. (1) *P. ampla*. (2) *P. chalara*. (3) *P. goetheana*. (4) *P. papalis*. (5) *P. diamesa*. (6) *P. phyxis*. (7) *P. mitra* (P. trachodes) and (8) *P. sinuosa*. "a" and "b" stand for different individuals. S = stacked dataset. SC = stacked dataset with only complete specimens and SCUB = stacked dataset with only complete and unblurry specimens.</figcaption></figure>

### 2.3.4 MobileNet convolutional neural networks

The CNNs are constructed by node layers including input layers which transfer their information in the form of node connections with different weights and threshold values into hidden layers, the convolutional layers, which process and transform the information into the next layers. Each convolutional layer has a different size and number of filters. A filter can be seen as a small grid of pixels, with different pixel values in each grid corresponding to a specific color value. This grid will go through an entire image in a sliding (convolving) way and transform the new values to the next layer that will process the image in a different or similar way. Early layers could, for example, easily detect edges, circles or corners, and later layers can even recognize more specific objects (Krizhevsky et al., 2012).
The network used for training specimens in classification is the Keras implementation of MobileNet (Howard et al., 2017), which is a convolutional neural network architecture (Chollet, 2015). Since MobileNet is a relatively small model, less regularization and data augmentation procedures are needed because smaller models have fewer problems with overfitting (Howard et al., 2017).

The input size of the images in the network was set to 256 x 256 x 3 (number three stands for red, green and blue (RGB) colors or channels), dropout was set to 0.15 for layers trained by ImageNet inside the MobileNet architecture and an average pooling was used. An added dropout layer was set to 0.5 and added after the MobileNet convolutional layers to prevent overfitting by randomly switching off some percentages of neurons in the model. Finally, a dense layer was added, which is the most commonly used layer in neural network models. It performs a matrix-vector multiplication, for which values are parameters and which can be trained and updated with backpropagation, and the dense layer was set to eight outputs corresponding to the number of species. The SoftMax activation used here converts the values into probabilities. The optimizer used was “Adam”, a stochastic gradient descent, and the loss function used was “Categorical Crossentropy”. The batch size of 64 resulted in 100 steps per epoch and only three epochs were necessary for the training, for the simple reason of avoiding any overfitting models. After three and sometimes four epochs, the validation accuracy does not increase further, and the loss becomes bigger (see Tables 2.S1–2.S3 in the Supplement 2.1 for an example of a MobileNet run on five epochs). For each dataset, since we used the train-test split function, the model was run 10 times to obtain good enough average accuracies.
## 2.4 Results

### 2.4.1 CNN accuracies

The MobileNet convolutional neural network model used here resulted in an average validation accuracy of 88.46 % for 10 runs of the “S” dataset, 92.13 % average accuracy for the “SC” and 92.39 % for the “SCUB” datasets for 10 runs on each dataset (Table 2.3). It is important to investigate how each run was performed, since it may vary a lot, especially by looking at each individual species’ performance (Table 2.3). Although there are codes that can equally select 15 % from each species, we chose not to use that option here because we also wanted to see how the model performs without selecting all general forms for each species. The total time to run MobileNet on all datasets (total of 30 times) was around 8.5 h, 10–15 min for each run. In general, the “S” dataset with complete specimens had the smallest difference between the lowest and highest accuracies over its 10 runs. The dataset consisting of stacked, complete, clear and crisp specimens also had low variation between the highest and lowest accuracies.

<table><caption>**Table 2.3.** Validation accuracies for all the studied species over 10 runs for each one of the three datasets, and their average values based on results of the species accuracies from Supplement 2.2.</caption><thead><tr><th colspan="10">S</th></tr><tr><th>Run</th><th>P. ampla</th><th>P. chalara</th><th>P. diamesa</th><th>P. goetheana</th><th>P. mitra</th><th>P. papalis</th><th>P. phyxis</th><th>P. sinuosa</th><th>Average</th></tr></thead><tbody><tr><td>1</td><td>100.00</td><td>100.00</td><td>33.33</td><td>100.00</td><td>90.91</td><td>88.89</td><td>100.00</td><td>92.86</td><td>87.88</td></tr><tr><td>2</td><td>100.00</td><td>67.74</td><td>100.00</td><td>94.74</td><td>100.00</td><td>95.35</td><td>80.00</td><td>80.00</td><td>89.69</td></tr><tr><td>3</td><td>100.00</td><td>100.00</td><td>27.78</td><td>100.00</td><td>78.57</td><td>31.25</td><td>57.14</td><td>53.33</td><td>62.03</td></tr><tr><td>4</td><td>100.00</td><td>95.65</td><td>64.29</td><td>100.00</td><td>100.00</td><td>53.66</td><td>87.50</td><td>100.00</td><td>83.54</td></tr><tr><td>5</td><td>100.00</td><td>100.00</td><td>93.33</td><td>100.00</td><td>100.00</td><td>92.86</td><td>100.00</td><td>100.00</td><td>97.47</td></tr><tr><td>6</td><td>100.00</td><td>100.00</td><td>100.00</td><td>95.65</td><td>100.00</td><td>88.37</td><td>100.00</td><td>100.00</td><td>96.38</td></tr><tr><td>7</td><td>100.00</td><td>100.00</td><td>44.44</td><td>90.48</td><td>100.00</td><td>97.96</td><td>77.78</td><td>100.00</td><td>91.50</td></tr><tr><td>8</td><td>100.00</td><td>100.00</td><td>78.57</td><td>100.00</td><td>100.00</td><td>97.30</td><td>93.33</td><td>93.33</td><td>96.38</td></tr><tr><td>9</td><td>100.00</td><td>100.00</td><td>100.00</td><td>85.71</td><td>62.50</td><td>75.00</td><td>71.43</td><td>88.89</td><td>83.91</td></tr><tr><td>10</td><td>100.00</td><td>100.00</td><td>0.75</td><td>91.67</td><td>96.43</td><td>96.15</td><td>100.00</td><td>100.00</td><td>95.84</td></tr><tr><td>Average</td><td>100.00</td><td>96.34</td><td>64.25</td><td>95.82</td><td>92.84</td><td>81.68</td><td>86.72</td><td>90.84</td><td>88.46</td></tr></tbody></table>
<table><caption>SC</caption><thead><tr><th>Run</th><th>P. ampla</th><th>P. chalara</th><th>P. diamesa</th><th>P. goetheana</th><th>P. mitra</th><th>P. papalis</th><th>P. phyxis</th><th>P. sinuosa</th><th>Average</th></tr></thead><tbody><tr><td>1</td><td>100.00</td><td>100.00</td><td>80.00</td><td>88.89</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>96.46</td></tr><tr><td>2</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>87.10</td><td>100.00</td><td>92.31</td><td>94.69</td></tr><tr><td>3</td><td>100.00</td><td>100.00</td><td>75.00</td><td>100.00</td><td>100.00</td><td>94.12</td><td>100.00</td><td>100.00</td><td>96.46</td></tr><tr><td>4</td><td>100.00</td><td>100.00</td><td>72.73</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>97.35</td></tr><tr><td>5</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>96.97</td><td>100.00</td><td>100.00</td><td>99.12</td></tr><tr><td>6</td><td>100.00</td><td>100.00</td><td>83.33</td><td>100.00</td><td>100.00</td><td>100.00</td><td>0.00</td><td>0.00</td><td>99.12</td></tr><tr><td>7</td><td>75.00</td><td>70.00</td><td>0.00</td><td>100.00</td><td>12.50</td><td>60.61</td><td>50.00</td><td>100.00</td><td>47.79</td></tr><tr><td>8</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>79.50</td><td>0.00</td><td>100.00</td><td>99.12</td></tr><tr><td>9</td><td>100.00</td><td>100.00</td><td>66.67</td><td>100.00</td><td>93.10</td><td>80.77</td><td>100.00</td><td>100.00</td><td>92.04</td></tr><tr><td>10</td><td>100.00</td><td>100.00</td><td>90.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>99.12</td></tr></tbody><tfoot><tr><td>Average</td><td>97.22</td><td>97.00</td><td>76.77</td><td>98.89</td><td>90.56</td><td>89.91</td><td>75.00</td><td>89.23</td><td>92.13</td></tr></tfoot></table>

<table><caption>SCUB</caption><thead><tr><th>Run</th><th>P. ampla</th><th>P. chalara</th><th>P. diamesa</th><th>P. goetheana</th><th>P. mitra</th><th>P. papalis</th><th>P. phyxis</th><th>P. sinuosa</th><th>Average</th></tr></thead><tbody><tr><td>1</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>84.62</td><td>100.00</td><td>50.00</td><td>94.37</td></tr><tr><td>2</td><td>80.00</td><td>75.00</td><td>60.00</td><td>100.00</td><td>100.00</td><td>70.00</td><td>100.00</td><td>100.00</td><td>85.92</td></tr><tr><td>3</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>88.24</td><td>100.00</td><td>100.00</td><td>100.00</td><td>97.18</td></tr><tr><td>4</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>93.33</td><td>100.00</td><td>85.71</td><td>97.18</td></tr><tr><td>5</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>94.12</td><td>94.12</td><td>100.00</td><td>98.59</td></tr><tr><td>6</td><td>100.00</td><td>100.00</td><td>80.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>98.59</td></tr><tr><td>7</td><td>100.00</td><td>100.00</td><td>71.43</td><td>100.00</td><td>81.82</td><td>81.82</td><td>0.00</td><td>100.00</td><td>92.96</td></tr><tr><td>8</td><td>100.00</td><td>100.00</td><td>0.00</td><td>100.00</td><td>57.14</td><td>77.78</td><td>33.33</td><td>40.00</td><td>73.24</td></tr><tr><td>9</td><td>100.00</td><td>94.12</td><td>62.50</td><td>100.00</td><td>100.00</td><td>100.00</td><td></td><td>100.00</td><td>94.37</td></tr><tr><td>10</td><td>100.00</td><td>100.00</td><td>75.00</td><td>100.00</td><td>93.75</td><td>88.24</td><td>100.00</td><td>75.00</td><td>91.55</td></tr></tbody><tfoot><tr><td>Average</td><td>97.78</td><td>96.91</td><td>74.89</td><td>100.00</td><td>92.09</td><td>88.99</td><td>80.83</td><td>85.07</td><td>92.39</td></tr></tfoot></table>

### 2.4.2 Confusion matrices

The neural networks also produced confusion matrices for each run. Since the training and validation sets were randomized, we therefore created three average confusion matrices (Fig. 2.5), one for each type of dataset. Since the number of specimens used for validation varies, we calculated an average value for the validation size as well. In these confusion matrices the y-axis shows the actual species, while the x-axis shows predicted ones. Each box shows the average accuracy based on the validation set.
What is further observed is the fact that closely related species (i.e., morphospecies situated
along an evolutionary lineage) are often mistaken for each other. This is true especially for
those species with more than one neighboring species along a lineage, which is the case for all
species studied here with the exception of the lineage end members, e.g., *P. ampla* and *P.
goetheana*. A very remarkable point is that *P. diamesa* appears to often be misidentified as *P.
papalis*, more frequently than *P. papalis* is misinterpreted as *P. diamesa*, which results in the
average precision value being lower in *P. diamesa* compared to the rest of the species, while *P.
papalis* has a lower recall than precision value.
<figure><img src="image_10.png" /><figcaption>**Figure 2.5.** Average confusion matrices for the “S”, “SC” and “SCUB” dataset. The numbers inside the matrices shows the average validation accuracy of specimens in each class that has been correctly or incorrectly classified. The numbers under each species name are the total number of true or predicted labels.</figcaption></figure>
Figure 2.6 displays the calculated average precision, recall and F1 score for all species based on the confusion matrices. The precision value also means the correct prediction value, and can be simplified by the following Eq. (1):

$$Precision = \frac{\text{Number of specimens classified as a class and also belonging to that class}}{\text{All specimens classified as that particular class}} \qquad (1)$$

The recall values show that not all specimens belonging to a class have been classified to the correct class, similar to the accuracy, which is the number of specimens correctly classified divided by the total number of specimens, as in the Eq. (2):

$$Recall = \frac{\text{Number of specimens in a class that were correctly classified}}{\text{Total number of specimens in that class}} \qquad (2)$$

The F1 score is an average value of this and shows the average between precision and recall written like Eq. (3):

$$F1 = \frac{2*(Precision*Recall)}{Precision+Recall} \qquad (3)$$

The average F1 scores based on the confusion matrices are 89.05 %, 93.26 % and 91.72 % for the “S”, “SC” and “SCUB” datasets, respectively. This implies that the best result for the F1 score is obtained when the CNN was trained on the “SC” dataset.
Average Precision, Recall and F1 score
S dataset

<figure><img src="image_11.png" /></figure>

Average Precision, Recall and F1 score
SC dataset

<figure><img src="image_12.png" /></figure>

Average Precision, Recall and F1 score
SCUB dataset

<figure><img src="image_13.png" /><figcaption>**Figure 2.6.** Average precision, recall and F1 score calculated from the average confusion matrices based on the S, SC and SCUB datasets.</figcaption></figure>
### 2.4.3 Testing the predictive models

Once a high validation accuracy (i.e., over 90%) had been obtained for each dataset following the training process, parameters were saved and predictive models were formed. The idea is to explore how the classification tool classifies *Podocyrtis* species and with what level of confidence. Therefore, a total of 22 specimens of *Podocyrtis* obtained from ODP Leg 171B, Hole 1051A (Blake Nose, western North Atlantic Ocean), DSDP Leg 95, Hole 612 (New Jersey slope, North Atlantic Ocean) and ODP Leg 115, Hole 711A (Madingley Rise, Indian Ocean) were used as a test dataset. Slides were prepared with Canada balsam and photographs were taken with a Leica transmitted light microscope to which an AmScope MU1003 digital camera was attached. Images were then segmented with ImageJ, and rotated and resized with a Python script as described above. Some images were further cropped, but no background particles were removed. Tables S4–S6 in Supplement 2.1 display how each specimen from the different locations was classified using different predictive values, a comparison with classification by cropping images that appeared very tiny in relation to the entire image and a comparison of removing background particles of those specimens which displayed them. The main result shows that *P. sinuosa* is often misinterpreted as *P. papalis* even though *P. papalis* is almost always classified correctly. There are significant morphological differences between the species trained in the neural network and the test set. Here, the best predictive model to use is the “S” dataset and the worst one is “SCUB” (Table 2.4), which is quite the reverse from the validation set based on the tropical Atlantic specimens from the Demerara Rise trained in this network.

Results obtained after the model “S” was applied on material from the North Atlantic and Indian Oceans were 13 out of 22 specimens classified correctly without any manual fixation, which corresponds to 59% accuracy. The accuracy of model “SC” was raised to 68% after manually
cropping images for specimens that appeared smaller, while it was increased up to 77 % by removing background particles appearing in the images. The “SC” model produced worse results. For all images without any manual fixation, the accuracy obtained was 45 %, but increased up to 50 % after adding manual cropping and to 54.5 % after adding the segmentation. The “SCUB” model had an accuracy of 41 % for all three of the different image fixations. However, in all cases, in terms of neighboring species, at least 20 specimens were correctly classified as a neighboring species which translated into an accuracy of at least 90.9 %.

<table><caption>**Table 2.4.** Result of all the 22 specimens including necessary manual cropping and segmentation from ODP Leg 115, Hole 711A from the Indian Ocean; DSDP Leg 95, Hole 612; and ODP Leg 171, Hole 1051A from the western North Atlantic Ocean which were classified using different parameters trained on the three different datasets, "S", "SC" and "SCUB".</caption><thead><tr><th rowspan="2">Classification</th><th colspan="3">All</th><th colspan="3">Manual cropping</th><th colspan="3">Manual segmentation</th></tr><tr><th>Correct</th><th>Incorrect</th><th>Uncertain</th><th>Correct neighbouring species</th><th>Correct</th><th>Incorrect</th><th>Uncertain</th><th>Correct neighbouring species</th><th>Correct</th><th>Incorrect</th><th>Uncertain</th></tr></thead><tbody><tr><td>S</td><td>13</td><td>7</td><td>2</td><td>20</td><td>15</td><td>5</td><td>2</td><td>21</td><td>17</td><td>3</td><td>2</td></tr><tr><td>SC</td><td>10</td><td>10</td><td>2</td><td>21</td><td>11</td><td>9</td><td>2</td><td>21</td><td>12</td><td>8</td><td>2</td></tr><tr><td>SCUB</td><td>9</td><td>11</td><td>2</td><td>22</td><td>9</td><td>11</td><td>2</td><td>22</td><td>9</td><td>11</td><td>2</td></tr><tr><td>Total number of specimens</td><td colspan="3">22</td><td colspan="3"></td><td colspan="3"></td></tr></tbody></table>

## 2.5 Discussion

### 2.5.1 MobileNet performance and accuracy

Dedicated to embedded systems and smartphones for which low latency and real-time execution are key demands, the advantage of using the MobileNet architecture is that it is extremely light and small (in terms of coding and weight of models). It is fast, with an only slight degradation in inference accuracy according to the gain of the consumed resources, and easily configurable to improve detection accuracy (Howard et al., 2017). When tested on Im2GPS, a dataset which
gives geolocation from images taken from different spots around the world, the accuracy of MobileNet was about 1% higher compared to GoogleNet, whereas it used 2.5 times less computation and, as cited in Howard et al. (2017), “MobileNet is nearly as accurate as VGG16 while being 32 times smaller and 27 times less compute intensive”. The study by Howard et al. (2017) presents extensive experiments on resource and accuracy trade-offs and shows strong performance of MobileNet compared to other popular models on ImageNet classification. This is the reason why some works (Rueckauer et al., 2021) start to deploy MobileNet also on neuromorphic hardware such as Loihi (Davies et al., 2021). Although the development of AI has been based until now on software bricks installed on big data centers, the current multiplication of connected objects requires decentralization. The new AI revolution now involves development of specific electronic components with very promising results.

The images that we have used were transformed to RGB-colored because the pretrained weights of ImageNet are only compatible for RGB-colored images, as this is also the case of the whole architecture of MobileNet; the idea here is to apply a depthwise convolution for a single filter for each unique input channel. The use of neural network models, equally adapted for grayscale images, could also decrease the energy consumption greatly. In terms of resolution, MobileNet resizes images into a lower resolution. In most cases this did not affect the result, but it is plausible that in a few cases the neural network was not able to see the position of the lumbar stricture, which is an important distinguishing character. In any case, Renaudie et al. (2018) also commented on the resolution loss due to resizing, as the inner spicules in *Antarctissa* species disappeared, which are crucial for species identification.

The species with the highest F1 score (Fig. 2.5) were *P. goetheana* (94%-97%) and *P. ampla* (90%-99%). A reason for this might be that both species are at the end of the *Lampterium* and
*Podocyrtoges* lineages and have only one closely related species, while the others have two. For *P. chalara*, *P. sinuosa* and *P. mitra*, a reduction in the number of analyzed specimens increased the precision and increased the recall value for *P. mitra*, giving *P. mitra* an overall better F1 score with reduction of specimens. This is expected, since the variability decreases when removing more imperfect specimens but performs better for determining unbroken and clear specimens. A reduction of specimens for *P. goetheana* did not make a big difference to precision but it did increase the recall. In general, *P. diamesa* has both the lowest recall (69%–85%) and precision (80%–84%) values. Although *P. diamesa* and *P. papalis* are often mistaken for one another, it is mostly *P. diamesa* that is misidentified as *P. papalis*, which may be due to the fact that late morphotypes of *P. papalis* resemble *P. diamesa* to some extent. The distinguishing character of this species is that the overall shape of *P. papalis* is in most cases rather fusiform with a larger thorax than abdomen, which is only partially developed. *Podocyrtis diamesa* is much larger (even though this does not seem to matter since all images are resized to equal sizes), the size of the thorax and abdomen is often more similar, and the lumbar structure is more prominent. Although late forms of *P. papalis* do resemble *P. diamesa*, they do not co-exist at the same time interval (the true *P. diamesa* morphospecies disappeared well before the appearance of the late *P. papalis* forms) and they are smaller.

The highest F1 score for all species except *P. diamesa* comes from using a model trained on complete specimens ("SC"), regardless of quality. *Podocyrtis phyxis* is one example of a species that has the best performance in the "SC" dataset. The overall dataset of *P. phyxis* consists of many specimens that are broken and missing the apical horn; therefore, there is a significant reduction of specimens when using the datasets with only complete specimens. The number of specimens present for *P. phyxis* in the "S" dataset was 64. This number dropped down to 22
specimens in the “SC” dataset and then further to 16 specimens in the “SCUB” dataset. When it comes to *P. sinuosa*, there is a substantial reduction due to many blurry specimens, which is likely the result of the mounting media. A total of 49 blurry specimens were removed from the “SC” dataset (78) compared to the “SCUB” dataset (29), and the overall result seems to have been improved somewhat by removing specimens.

To summarize, our work has about 91 % accuracy if we exclude the tests run with the unstacked (U) dataset (Supplement 2.1, Table S7), which not only consisted of blurry unfocused images but also kept some touching particles. Nevertheless, it has an overall similar accuracy of about 72 % to the work produced by Renaudie et al. (2018) with the same neural network. These authors used images as they appear under the microscope, without stacking or image processing. To produce more datasets with these types of images, one can expect a slightly lower accuracy than when images are processed. Renaudie et al. (2018) also included a substantial number of unidentifiable specimens. If these unidentifiable images were ignored, results would probably come close to 90 % accuracy. In our case, all specimens are identifiable by our CNN as any of the eight possible *Podocyrtis* species, even if the specimen in question is not a *Podocyrtis*. A solution for this issue is to perhaps apply parallel neural networks with a hierarchical architecture, similar to the one that Beaufort and Dollfus (2004) applied for SYRACO. One suggestion could be first to classify radiolarians and non-radiolarian particles, with a second step classifying radiolarians into higher taxonomic orders (Spumelleria, Nassellaria and unidentified broken radiolarians) and finally, a last step leveling down to species, genus and/or family levels. Itaki et al. (2020) went with another approach. These authors focused on the identification of one single species, *Cycladophora davisiana*, but they also used the morphologically similar species, *Cycladophora bicornis*, as another class, to avoid
this species being wrongly interpreted as C. davisiana. Thereafter, they used the classes "centric diatoms", "all other radiolarians" and "all other objects".

It is worth noting that validation accuracies and F1 score values may appear high since we do not have a large dataset and the images in the datasets are in the same orientation and rotational angle, centralized in the middle. As mentioned earlier, species are carefully selected to avoid including any intermediate forms in the dataset. 5.2 Predictive models Given that our initial analysis was performed with material coming from the equatorial Atlantic (ODP Leg 207), we wished to consider a different dataset to test the predictive models, which consisted of images of specimens coming from the North Atlantic (DSDP Leg 95, ODP Leg 171) and the Indian Ocean (ODP Leg 115). In addition, radiolarians were mounted in a different mounting media (Canada balsam) and we used a different microscope. In most cases, particles were segmented properly in the segmentation process. However, some specimens were still attached to other particles, which could also result in that the specimens did not fill out the entire images. To save time and effort, we did not crop or remove background particles at first. These specimens could still be classified correctly. The results improved after the specimens were cropped or had their background particles removed, at least by using the predictive model trained by the "S" dataset. *Podocyrtis papalis* is almost always correctly interpreted. The reason might be the large number of specimens used in our dataset and the large number of morphological differences within the species. As mentioned earlier *P. sinuosa* is often misinterpreted as *P. papalis*. This is almost always the case for all *P. sinuosa*, whether we use the "SC" or "SCUB" datasets. The main reason for this probably lies in the fact that the morphotypes of *P. sinuosa* coming from the North Atlantic and the Indian oceans differ significantly (Fig. 2.2) from the ones trained in the initial neural network. Two other factors may contribute to this: first the
decrease in *P. sinuosa* specimens when passing from the “S” dataset to the “SC” and “SCUB” datasets, as explained above; and secondly, the image quality, since *P. sinuosa* appear very whitish in the trained neural network. One *P. chalara* was first classified as *P. mitra* before it was cropped. One possible reason may have been that the pores of the uncropped version appeared smaller, as in *P. mitra*. After cropping out unnecessary space, *P. chalara* appeared larger and could be correctly classified. In one case, one specimen, which was clearly a late *P. mitra* (according to preference of the author and not *P. trachodes*), was completely wrongly identified as either *P. phyxis* by using the “S” predictive model, *P. ampla* by using the “SC” predictive model or *P. goetheana* by using the “SCUB” predictive model. The reason for this may be due to the mounting media or preservation, because the pore space appears blacker or cleaner, similar to *P. phyxis* or *P. ampla*, which are generally larger from the tropical Atlantic assemblages trained in this network, and resizing the images may make them appear to be in a better resolution, with no white “dirt” between the pores and within the pore space. *Podocyrtis goetheana* also have gigantic pores and a lot of black space.

Most often it is closely related species that are mistaken for each other, as observed in the training and validation. It can be observed in Table 2.4 that even if specimens were not always interpreted as the right species, they were almost always interpreted as a neighboring species along a lineage. It was also discussed by Renaudie et al. (2018) that closely related species tend to be misinterpreted as each other due to morphological similarities, which is also confirmed in this work.

### 2.5.2 Species choice and their image properties

Akin to the study of Renaudie et al. (2018) conducted on Neogene radiolarians, we used the MobileNet neural network to classify closely related species of the Eocene genus *Podocyrtis*.
We did not, however, use images as they appeared under the microscope, since we used
software and codes for image processing which can easily process several images into equal
settings at once and do not only increase the image quality but also save a lot of time and effort.

Renaudie et al. (2018) chose to select all specimens present in a slide that could somehow be
classified with reliable confidence. The same approach was followed here, but most of the
samples were pre-selected knowing that some typical morphotypes existed in them. We used
specimens for which species identification and classification was certain in most cases, meaning
specimens which could be instantly recognized to one species, and left out uncertain
intermediate forms. We also used specimens which were preserved nearly completely, even
though many experts are often able to identify specimens based on even small fragments to at
least a genus level. Smaller identifiable fragments would probably require a huge amount of
data to train. The samples obtained here have excellent preservation and finding broken
fragments in, for example, dinoflagellate cysts seem more likely than finding broken pieces of
radiolarians, provided they have not been crushed by mounting.

**2.6 Conclusions**

The goal of this study was to create an automatic classification tool to allow AI-based
identification of middle Eocene *Podocyrtis* species which would achieve the highest possible
accuracy after training the MobileNet CNN based on a dataset of 1085 images of *Podocyrtis*
morphotypes classified as eight different species.

Regarding our first question stated in the introduction, “How well can the MobileNet
convolutional neural network classify closely related species of the genus *Podocyrtis*?”, we
showed that specimens which belong to *Podocyrtis* species can be classified automatically with
a high accuracy (91 % confidence). Best results were obtained by using datasets with improved
quality (but a smaller number of images), both according to overall accuracy and the F1 score
values. However, tests on *Podocyrtis* species from the North Atlantic and Indian Ocean work
best by using the predictive model trained by the normal stacked dataset, consisting of more
specimens but with a mix of broken, complete, blurry and clear images. This suggests that a
higher variance of morphotypes could be applied to the datasets. In conclusion, this
identification tool works well for classification of *Podocyrtis* species, although it could still be
further improved by adding additional closely resembling species of *Podocyrtis* that were not
present or very rare in our material.

Regarding our second scientific question, “How well can the predictive model classify *Podocyrtis* species under different processing settings and using material coming from different parts of the world’s oceans?”, we establish that the predictive models also work well for classifying images taken by different microscopes, but might in some cases require adjustment of the clarity settings and images taken by different mounting medias.

This study could be further improved by including additional morphospecies of *Podocyrtis* in
the datasets and more specimens and morphotypes, especially from many other different
oceanic realms. Another improvement to the neural network would be to detect *Podocyrtis*
species or other taxa of interest among hundreds to thousands of other objects. This could
perhaps be solved by classifying every object, as, for example, done in Tetard et al. (2020) and
Itaki et al. (2020), or by applying a parallel network approach (Beaufort and Dollfus, 2004) first
that filters away objects of no interest in different steps. For example, as a first step, this would
involve the classification of radiolarians and non-radiolarians, and as a second step, the
exclusion of all non-radiolarians. It could also be beneficial to create a network inspired by
MobileNet but adapted to grayscale images, in order to become more energy efficient and have
a more appropriate resolution that detects small crucial details in radiolaria classification.

Data availability

Microscope slides from Leg 207, Hole 1259A and 1260A, which were used for training and
validation of the neural network are stored at the University of Lille, France, and slides from
ODP Leg 115, Hole 711A, DSDP Leg 95, Hole 612 and ODP Leg 171B, Hole 1051A, which
were used for testing the CNN and are stored at the Museum für Naturkunde in Berlin,
Germany. Datasets (https://doi.org/10.57745/G7CHQL, Carlsson, 2022) and codes
(https://doi.org/10.57745/J4YL4I, Carlsson and Laforge, 2022) are published in the University
of Lille repository at Recherche Data Gouv.

Supplement

The supplement related to this article is available online at: https://doi.org/10.5194/jm-41-165-2022-supplement.

Acknowledgements

This study was supported by the French government through the program “Investissements d’avenir” (I-ULNE SITE/ANR-16-IDEX-0004 ULNE) managed by the National Research Agency. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 847568. It was also supported by UMR 8198 Evo-Eco-Paléo and IRCICA (CNRS and Univ. Lille USR-3380).

We would also like to give special thanks to Mathias Meunier for giving opinions on taxonomy
and thanks to Hammouda Elbez who was a huge help with coding issues. Also, a huge thanks
to David Lazarus for discussion on the topic and for facilitating the access of VC to the Museum
für Naturkunde in Berlin to take images of material from ODP Leg 171B, Hole 1051A; DSDP Leg 95, Hole 612; and ODP Leg 115, Hole 711A.
# CHAPTER 3: Morphometrics and machine learning discrimination of the middle Eocene radiolarian species *Podocyrtis chalara, Podocyrtis goetheana* and their morphological intermediates

Francisco Pinto a, Veronica Carlsson a,b*, Mathias Meunier a, Bert Van Bocxlaer a, Hammouda Elbez b, Marie Cueille a, Pierre Boulet b, Taniel Danelian a

<footnote>a Univ. Lille, CNRS, UMR 8198, Evo-Eco-Paleo, F-59000 Lille, France</footnote>

<footnote>b Univ. Lille, CNRS, CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, UMR 9189, F-59000 Lille, France</footnote>

<footnote>* Corresponding author. E-mail address: [veronica.carlsson@univ-lille.fr](mailto:veronica.carlsson@univ-lille.fr)</footnote>
Author contributions

Francisco Pinto collected and classified the images and wrote the initial draft of the manuscript; he led the work on the description of the intermediate forms; he conducted the LDA analysis and participated in the neural network analysis.

Veronica Carlsson is the corresponding author and formulated the research methodology in discussion with the other authors. She also confirmed the classification of end member and intermediate species and contributed with more images, took part in the writing and reviewing of the manuscript; she led the discussions and analyses on neural networks.

Mathias Meunier contributed to the supervision of image collection and preparation and led the actions on their LDA analysis. He also contributed to the writing and revision of the manuscript.

Bert Van Bocxlaer contributed to the design of the research methodology, especially concerning the LDA analyses, and has been part of the analytical work and discussion. Has was also involved in the writing and revision of the manuscript.

Hammouda Elbez coded and performed the neural network experiments and he contributed to the writing, discussion and revision regarding the neural network part.

Pierre Boulet contributed to the discussion and supervision, especially regarding the neural network part.

Marie Cueille contributed to the initial steps of this study that led to the discovery and first description of the intermediate morphotypes.
Taniel Danelian initiated this study and led the discussions that formulated the research methodology; he supervised the whole project and contributed to the writing and revision of the manuscript.

## Abstract

We present various approaches to distinguish the middle Eocene species *Podocyrtis chalara* and *Podocyrtis goetheana*, which are end members of a trajectory of phenotypic change, and their intermediate morphogroups. We constructed a set of thirteen traditional morphological variables to classify the entire morphological variability encompassed by the two morphospecies and their intermediates *Podocyrtis* sp. cf. *P. chalara* and *Podocyrtis* sp. cf. *P. goetheana*. We used two methods of classification, namely Linear Discriminant Analysis (LDA) and machine learning using artificial neural networks. LDA performed on the morphometric data reveals a good discrimination for *P. chalara*, *P. goetheana* and *Podocyrtis* sp. cf. *P. goetheana*, but not for *Podocyrtis* sp. cf. *P. chalara*. We used three approaches of machine learning based on different neural networks: Convolutional Neural Networks (CNNs) and two Spiking Neural Networks (SNNs). Each of these neural networks was trained based on classified images of the two morphospecies and their morphological intermediates, thus constituting a different set of input data than the morphometric dataset for LDA. The neural network approaches identified the same three morphospecies recognized by LDA from a dataset of traditional measurements, i.e. *P. chalara*, *P. goetheana* and *Podocyrtis* sp. cf. *P. goetheana*, with up to 92 % accuracy. Our results highlight the great potential and promising perspectives of machine learning and neural networks in the application of image-based object recognition for morphological classification, which may also contribute to more objective taxonomic decisions.
**Keywords:** Morphometrics; Artificial Intelligence; Convolutional Neural Networks; Spiking Neural Networks; Radiolarians; Automated identification

**3.1 Introduction**

Polycystine radiolaria are one of the oldest known Rhizarian lineages, with a fossil record stretching back to the Early Cambrian (Obut and Iwata, 2000; Pouille et al., 2011) and is thus of much interest for a number of evolutionary studies (e.g., Danelian and Johnson, 2001, Danelian et al., 2014, Renaudie and Lazarus, 2003, Tetard et al., 2017).

Since the early stages of the Deep-Sea Drilling Program, the Cenozoic record of polycystine radiolaria has allowed us to establish their evolutionary and biostratigraphic significance, especially based on representatives of the Eocene genus *Podocyrtis*. (Sanfilippo and Riedel, 1970; Riedel, 1971; Moore, 1972; Riedel and Sanfilippo, 1978). Calibrated initially to the magnetostratigraphic time scale (Sanfilippo and Nigrini, 1998), the middle Eocene tropical radiolarian zones are now tied to orbital chronology (Meunier and Danelian, 2022), which provides the highest resolution of temporal control possible today and allows to define biostratigraphic events more accurately. Indeed, many of the middle Eocene biozones are based on the evolution of the various lineages of the genus *Podocyrtis*, which often relate to gradual anagenetic changes in phenotypes, as documented in several evolutionary lineages since the early 1970s (Sanfilippo and Riedel 1970, 1992; Riedel, 1971). For example, the bases of biozones RP14 and RP15 are defined based on anagenetic phenotypic changes between the morphospecies *P. sinuosa* - *P. mitra* and *P. mitra* - *P. chalara*, respectively.

The *Podocyrtis (Lampterium)* lineage ends with the marked morphological transition of *Podocyrtis chalara* to *P. goetheana*. Interestingly, the first occurrence of *P. goetheana* defines
the base of biozone RP16 (Sanfilippo and Nigrini, 1998); an anagenetic transition was reported, but intermediate forms were never documented in detail. The absence of such a documentation has implications for our understanding of evolutionary changes in this *Podocyrtis* lineage, but as intermediate forms are poorly understood it also affects the recognition of the base of RP16. Here we examine the well-preserved radiolarian material of ODP Sites 1259 and 1260 from Demerara Rise (equatorial Atlantic Ocean), which present an exceptionally expanded Eocene sedimentary sequence. As such, this material offers an exceptional opportunity to study the morphological transitional forms between *P. chalara* and *P. goetheana*.

Within the abovementioned context, the principal aim of our study is to document morphological variation in the anagenetic sequence of *P. chalara* to *P. goetheana* with two different but complementary approaches to test the performance of various machine learning algorithms based on neural networks. To reach this objective, we first quantified morphological variation in the anagenetic transition between *P. chalara* and *P. goetheana* with traditional morphometrics, i.e., linear measurements, pore counts and associated ratios in the framework of qualitatively recognized morphological entities. This quantification of shape follows previous attempts to assess morphological changes in the *Podocyrtis* (*Lampterium*) lineage (Sanfilippo and Riedel, 1990; Rohlf and Bookstein, 1991; Danelian and Macleod, 2019; Watanabe et al., 2022). Using this morphometric framework of measurement data and *a priori* morphospecies assignments, we examined how well linear discriminant analysis allows to distinguish morphospecies as a baseline to test the performance of machine learning with neural networks.

Testing the capabilities of neural network approaches based on image recognition is a daunting task, because during the past couple of years, a variety of techniques involving Artificial Neural
Networks (ANNs) have been developed and improved. Convolutional Neural Networks (CNNs) have been specifically designed for analysis of visual data, and are now commonly used for image recognition, warranting detailed examinations of their performance in morphological classification, and, therewith, as a tool to inform, and potentially reach more objective taxonomic decisions. Indeed, CNNs are becoming well-integrated in various micropaleontological studies for automatic image recognition (i.e., Mitra et al., 2019, Hsiang et al., 2019; Marchant et al., 2020; Dollfus and Beaufort, 1999; Beaufort and Dollfus, 2004; Bourel et al., 2020; Itaki et al., 2020; Renaudie et al., 2018; Tetard et al., 2020). Regarding Eocene radiolaria, a recent study by Carlsson et al. (2022) applied a CNN to eight well-delimited morphospecies of the genus *Podocyrtis*, and documented the potential of this method under the simplified scenario when no morphological intermediates are present. Spiking Neural Networks (SNNs) present another type of neural network, which in addition to neuronal and synaptic states, they also incorporate a time component; this is why such networks can more closely mimic natural neural networks (Maass, 1997). SNNs have wide applicability, including modeling of natural systems such as the central nervous system of biological organisms, as well as for image analysis. Traditionally, SNNs were less accurate than other neural networks, but in recent years their performance has significantly improved; they are more appropriate to process spatio-temporal data, and they may use computational resources more effectively (Tavanaej et al., 2019). As such, an evaluation of SNNs in image recognition and biological classification seems warranted.

In this paper, we expand on previous work by Carlsson et al (2022) with CNNs by aiming to classify stacked and segmented images of the entire spectrum of morphological variation found between *P. chalara* and *P. goetheana*. As mentioned, we used morphometrics to document
shape variability, which we subjected together with *a priori* morphospecies assignments to LDA as a baseline to study the classification performance for image-based neural networks using a CNN, a Spike-timing-dependent plasticity (STDP)-based SNN and a SuperSpike-based SNN. This examination allows us to evaluate the use of imaging data and neural networks for automated classification in a complex case study with intermediate shapes. If the neural networks perform well, we would expect correct classification for each morphogroup. Additionally, the results should reflect those of the LDA analysis, if the morphometric documentation of shape variation is representative of the four morphospecies. Alternatively, neural networks may show differences compared to LDA. These distinctions could arise if neural networks fail to perform well, possibly due to unsuccessful training with the existing data. In opposite to that, the neural networks may perform better than LDA if the data supporting LDA lacks crucial shape information necessary for distinguishing between morphogroups based on the images. As such, we expect our study to shed light into future opportunities for automated biological classification of polycystine radiolaria and the use of neural networks in developing more objective taxonomic decisions.

## 3.2 Analyzed morphological groups

Plate 3.1 displays the entire range of morphological variability observed between *P. chalara* and *P. goetheana*. As linear discriminant analysis and the supervised learning of neural networks are based on *a priori* group assignments, we were required to assign this continuum of variation to a number of morphological groups. Based on extensive qualitative assessments and to challenge the employed classification algorithms we recognized four distinct morphological groups for the purpose of the current study, without currently being concerned
by the paleobiological/evolutionary status of each group. These morphogroups are briefly
presented below.

*Podocyrtis chalara* Riedel and Sanfilippo

Pl. 1, fig. A, B

1970 *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo, p. 535, pl. 12, figs. 2, 3.

1971 *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo: Moore, p. 743, pl. 3, figs. 5, 6.

1972 *Lampterium chalara* Riedel and Sanfilippo: Petrushevskaya and Kozlova, p. 543, pl. 32, fig. 12.

1978 *Podocyrtis (Lampterium) chalara* (Riedel and Sanfilippo): Riedel and Sanfilippo, p. 71, pl. 8, fig. 3, text-fig. 3.

2012 *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo: Kamikuri, p. 103, pl. 3, figs. 2a, 2b.

2012 *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo: Moore and Kamikuri, p. 9, pl. P7, fig. 8.

**_Distinguishing characters_**: We include here forms displaying twelve or less vertically well-aligned, subangular abdominal pores of similar size per horizontal row, illustrating the classic morphology of *P. chalara*. Specimens of this morphogroup display less than thirteen pores on the circumference of the abdomen.

*Podocyrtis* sp. cf. *P. chalara* Riedel and Sanfilippo

Pl. 1, fig. C, D
1972 *Lampterium* sp. G: Petrushevskaya and Kozlova, pl. 32, fig. 10.

1972 *Lampterium* sp. aff. *L. goetheana*: Petrushevskaya and Kozlova, pl. 32, fig. 13.

2022 *Podocyrtis* (*Lampterium*) *chalara* Riedel and Sanfilippo: Meunier and Danelian, p. 21, pl. 2.4.

**Distinguishing characters:** This morphogroup includes specimens that have a similar outline and appearance as *P. chalara*, but they display vertically misaligned subangular abdominal pores of different size. Specimens of this morphogroup may display vertical rows of pores that are shifted to the right or left compared to the rows of pores above and below, giving a twisted appearance for the rows of pores developed on the abdomen, with result a honeycomb-like pore pattern. These shifts may be so extensive that the arrangement of pores on the abdomen becomes chaotic, preventing the possibility to trace any apparent abdominal rows or vertical alignment.

*Podocyrtis* sp. cf. *P. goetheana* (Haeckel)

Pl. 1, figs. E - L

2006 *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo: Funakawa et al., p. 29, pl. P9, figs. 11a, 11b.

**Distinguishing characters:** This morphogroup is mainly characterized by an increase in total size, but with a significant reduction in the number of abdominal pores compared to both variants of *P. chalara*. It differs from *P. goetheana* in that the bars of the second row of abdominal pores are thicker and not always elongated, nor parallel to each other, as the
formation of the honeycomb-like pattern of pores becomes more apparent. This morphogroup displays a high degree of morphological variability.

*Podocyrtis goetheana* (Haeckel)

Pl. 1, figs. M - O

1887 *Cycladophora goetheana* Haeckel, p. 1376, pl. 65, fig. 5.

1970 *Podocyrtis (Lampterium) goetheana* (Haeckel): Riedel and Sanfilippo, p. 535.

1971 *Podocyrtis (Lampterium) goetheana* (Haeckel): Moore, p. 743, pl. 3, figs. 7, 8.

1972 *Lampterium* sp. aff. *L. goetheana* Petrushevskaya and Kozlova, pl. 32, fig. 14.

2005 *Podocyrtis (Lampterium) goetheana* (Haeckel): Nigrini et al., p. 45, pl. P5, figs. 11, 12.

2006 *Podocyrtis (Lampterium) goetheana* (Haeckel): Funakawa et al., p. 29, pl. P9, figs. 12a, 12b.

2012 *Podocyrtis (Lampterium) goetheana* (Haeckel): Kamikuri, p. 103, pl. 3, fig. 1.

2012 *Podocyrtis (Lampterium) goetheana* (Haeckel): Moore and Kamikuri, p. 9, pl. P7, fig. 9.

2022 *Podocyrtis (Lampterium) goetheana* (Haeckel): Meunier and Danelian, p. 21, pl. 2.5.

**Distinguishing characters:** This group includes only forms that display elongated straight bars formed at the level of the second horizontal row of pores on the abdomen. This feature is typical for *P. goetheana* as originally described, and, as mentioned above, the first occurrence of typical *P. goetheana* defines the base of the RP16 Zone.

**Plate 3.1.** Composite light micrographs of *Podocyrtis radiolaria* from ODP Site 1260, processed and scaled in ImageJ. (A) and (B) *Podocyrtis chalara*, samples: ODP 1260A-6R-5W, 63-65 cm and ODP 1260A-6R-5W, 20-
22 cm; (C) and (D) *Podocyrtis* sp. cf. *P. chalara*, samples: ODP 1260A-6R-4W, 68-70 cm and ODP 1260A-6R-5W, 20-22 cm; (E) to (L) *Podocyrtis* sp. cf. *P. goetheana*, samples: ODP 1260A-6R-4W, 68-70 cm; ODP 1260A-6R-5W, 15-17 cm and ODP 1260A-6R-5W, 87-89 cm; (M) to (O) *Podocyrtis goetheana*, sample: ODP 1260A-6R-1W, 58-60 cm.
A

<figure><img src="image_14.png" /></figure>

B

<figure><img src="image_15.png" /></figure>

C

<figure><img src="image_16.png" /></figure>

D

<figure><img src="image_17.png" /></figure>

E

<figure><img src="image_18.png" /></figure>

F

<figure><img src="image_19.png" /></figure>

G

<figure><img src="image_20.png" /></figure>

H

<figure><img src="image_21.png" /></figure>

I

<figure><img src="image_22.png" /></figure>

J

<figure><img src="image_23.png" /></figure>

K

<figure><img src="image_24.png" /></figure>

L

<figure><img src="image_25.png" /></figure>

M

<figure><img src="image_26.png" /></figure>

N

<figure><img src="image_27.png" /></figure>

O

<figure><img src="image_28.png" /></figure>

50µm
## 3.3 Materials and methods

### 3.3.1 Sediment samples

The material analyzed in this study consists of radiolaria obtained from an expanded middle Eocene siliceous chalk sequence drilled at ODP Sites 1259 and 1260 (Leg 207, Demerara Rise), located in the equatorial region of the Atlantic Ocean, 380 km offshore Suriname (Erbacher et al., 2004; Danelian et al., 2005). The middle Eocene sequence is particularly thick at Sites 1259 and 1260 and contains siliceous microfossils (radiolarians, diatoms) of an excellent state of preservation (Danelian et al., 2007; Renaudie et al., 2010; Meunier and Danelian, 2022, 2023). The part of the limestone sequence from Site 1260 that is studied here is dated by orbito-chronology (Westerhold and Röhl, 2013). More specifically, our samples span the interval between 41.24 Ma and 39.84 Ma. Site 1259 is dated via bio- and magneto-stratigraphy and was sampled in the interval between ~39.05 and 37.70 Ma.

### 3.3.2 Slide preparation

A combined total of 15 samples from both sites were chosen and prepared for microscopic observation using techniques described by Sanfilippo et al. (1985). A small quantity (~2 cm³) of unprocessed sediment was collected from each sample and dried overnight at 50°C to eliminate any residual water. After being weighed, sediment samples were soaked for 2 hours in a 500 mL polypropylene beaker containing 30 mL of 30 % hydrochloric acid (HCl), to dissolve their carbonate content and concentrate siliceous microfossils. A few mL of HCl were added at the end to confirm the end of the reaction. The residues resulting from the acid treatment was then washed by adding ~200 mL of distilled water. After 2 hours of decantation, excess water was carefully removed using a pipette. Residues were subsequently soaked for 2 hours in 30 mL of 10 % hydrogen peroxide (H₂O₂) to remove organic matter, and subsequently
washed through a 63 µm sieve using distilled water. The > 63 µm fraction was then exposed to
ultrasonic waves for 10 min, then passed again through the 63 µm sieve, and finally left to dry
overnight at 50°C. For each sample, ~2 g of dried residue was carefully spread on top of a slide
covered with several drops of Norland Optical Adhesive 61, then topped with a coverslip and
sealed by two minutes of exposure to UV light.

3.3.3 Microscopy and image processing

The resulting slides were analyzed with a Zeiss AXIO Images A2 microscope under transmitted
light at ×10 and ×20 magnifications. All specimens recognized qualitatively as *P. chalara*,
*Podocyrtis* sp. cf. *P. chalara*, *Podocyrtis* sp. cf. *P. goetheana* or *P. goetheana* were manually
photographed using the mounted Axiocam ERc5s with Zen 3.5 (blue edition) software. For
each specimen, a batch of 5-10 photographs were taken at different focal points to obtain a
series of images, which were stacked afterwards using Helicon Focus 7.7.0 (HeliconSoft) to
create a composite picture entirely in focus. The stacked images were subsequently retouched
with Paint3D to facilitate their automated segmentation. This last procedure was performed
with the ImageJ BioVoxxel plugin (Brocher, 2022) and the AutoRadio_Segmenter plugin
developed for ImageJ / Fiji (Tetard et al., 2020).

3.3.4 Morphometric analyses

As we aimed to subject specimens to discriminant analysis based on morphometric
measurements, it was essential that the documented morphological variables would adequately
capture shape variations present in between the four morphogroups. We first designed a set of
variables that would allow to compare with the variables used by Watanabe et al. (2022) on the
specimens of the *Lampterium* lineage from the Pacific Ocean. A subset of seven of these
variables was retained and supplemented with six newly proposed variables to result in a set of thirteen morphological variables that document well the morphological variation between the four morphogroups (Figure 3.1). The seven morphological variables proposed by Watanabe et al. (2022) are:

- W1: Maximal width of the cephalis

- W2: Maximal width of the thorax

- W3: Maximal width of the abdomen

- H1: Maximal height of the cephalis

- H2: Maximal height of the thorax

- H3: Maximal height of the abdomen

- TL: Total length or height of the specimen

and our six additional variables are:

- LP2: Maximum length of the second abdominal pore along the axis used to measure H3 (with the first pore being the one closest to the thorax-abdomen border)

- NPV: Number of abdominal pores aligned vertically along the axis used to measure H3 (on the front-facing side of the skeleton)

- NPH: Number of abdominal pores aligned horizontally behind the axis of W3 (on the front-facing side of the skeleton)

- R1: Maximum length of the second pore of the abdomen / H3

- R2: Number of abdominal pores aligned vertically behind the axis of H3 / H3

- R3: Number of abdominal pores aligned horizontally behind the axis of W3 / W3
<figure><img src="image_29.png" /><figcaption>**Figure 3.1.** Schematic representation of the eight skeletal variables measured for all specimens outlined in Table 3.1. Five additional variables related to pore counts and ratios are not illustrated. Abbreviations: W1: maximum width of the cephalis; W2: maximum width of the thorax; W3: maximum width of the abdomen; H1: maximum height of the cephalis without the apical horn; H2: maximum height of the thorax; H3: maximum height of the abdomen; TL: total length or height of the specimen without the apical horn; LP2: maximum length of the second abdominal pore.</figcaption></figure>

Quantifications of these variables (Table 3.S1 in Supplementary materials) were directly performed on a dataset of 214 photographs/specimens from samples 1260A-6R-1W, 58-60 cm, 1260A-6R-4W, 68-70 cm, 1260A-6R-5W, 15-17 cm and 1260A-6R-5W, 87-89 cm, outlined in Table 3.1, using the image processing and analysis software ImageJ (Schneider et al., 2012). All of these data were tabulated and then imported in the statistical environment R (v. 4.1.3; R Core Team 2022) for subsequent LDA using the packages MASS (v. 7.3-60; Ripley et al., 2013)
and vegan (v. 2.6-4; Oksanen et al., 2013). This analysis is a constrained ordination procedure that uses a linear combination of coefficients to maximize the distance between a priori defined groups, while minimizing the distance within each group (Venables and Ripley, 2002). As morphometric variables were expressed in both metric units and as ratios, the LDA was performed on data that were transformed to have the mean at zero- and one-unit standard deviation (z transformation). After subjecting the whole dataset to LDA we performed cross-validation by 100 replicates of randomly assigning 80 % of the data to a training dataset and the remainder to a testing set to evaluate the classification success of the LDA.

<table><caption>**Table 3.1.** Number of specimens analyzed per morphogroup and per sampled core interval.</caption><thead><tr><th>Samples</th><th>P. chalara</th><th>Podocyrtis sp. cf. P. chalara</th><th>Podocyrtis sp. cf. P. goetheana</th><th>P. goetheana</th><th>Total number of measured specimens per sample</th></tr></thead><tbody><tr><td>1260A 6R 1W 58-60cm</td><td>0</td><td>0</td><td>2</td><td>29</td><td>31</td></tr><tr><td>1260A 6R 4W 68-70cm</td><td>18</td><td>19</td><td>37</td><td>0</td><td>74</td></tr><tr><td>1260A 6R 5W 15-17cm</td><td>19</td><td>12</td><td>7</td><td>0</td><td>38</td></tr><tr><td>1260A 6R 5W 87-89 cm</td><td>34</td><td>9</td><td>28</td><td>0</td><td>71</td></tr></tbody></table>

### 3.3.5 Artificial Neural Networks

Artificial Neural Networks or simply neural networks are machine learning algorithms designed to simulate the decision-making processes of the human brain by analyzing and exploiting patterns in data (Yang and Yang, 2014). Prior to analysis, the data given to a network is split into two parts, one for training and one for testing, usually in a 80 to 20 ratio. The first set of data is used to train the neural network, so that to enable it to learn recognizing features and
patterns present in the data, whereas the second set of data is used to test the performance of
the network to classify cases based on the previously trained capabilities of recognition.

A type of ANNs that has been specifically designed to analyze visual data are CNNs, commonly
used for image recognition. They are designed to analyze visual data by considering the color
values of each pixel and by identifying patterns within images (Hijazi et al., 2015). CNNs utilize
a process known as convolution. Convolution can be described as a linear operation to
decompose the input image by sliding small windows known as filters or kernels over the input
image to construct layers that each obtain certain features. The convolutional layers in a CNN
modify gradually the image parameters, such as weights or bias, to learn and recognize specific
patterns or objects in the images. By adjusting these parameters through training, the network
aims to correctly classify the output given a particular input. When a CNN has multiple layers,
typically more than three, the procedure is referred to as deep learning, as each layer enables
the recognition of more and more advanced features in an image. As mentioned, SNNs consider
additionally the time factor, alike biological neurons, which use discrete spikes to compute and
transmit information, instead of characterizing neurons by a single, static continuous-valued
activation.

The hyperparameters (i.e. weights) of the neural network analyzed in this study were chosen
randomly; more specifically a value between -1 and 1 was chosen for the VGG16 and
SuperSpike-based networks, while a value network between 0 and 1 was chosen for the STDP-
based network neural. We analyzed two sets of stacked and segmented images. The first set
contained images with a priori assignments to the four morphogroups represented in section 2;
for the second set assignments were altered based on the results of LDA. In each case, we
performed ten runs per type of neural network used, with 20 epochs for each network, except
for the STDP-based network that was run with 100 epochs. An epoch simply means how many
passes it goes through the training set and updates parameters based on each pass. The following
neural networks were used to perform runs:

- Visual Geometry Group 16 (VGG16). This 16-layer deep CNN was used for its simplicity. For our analyses, we use transfer learning, meaning that the first 15 layers were already pre-trained based on a large-scale image dataset from ImageNet, and we only trained the last layer specifically using our data and PyTorch (Paszke et al., 2019). More information about VGG16 is provided by Simonyan and Zisserman (2015).

- STDP-based Spiking Neural Network (STDP-Network). This network contains convolutional and pooling layers that learn the features from the data using a Spike Timing Dependent Plasticity (STDP), a learning algorithm inspired by natural neurons. STDP adapts the synaptic connections between the neurons based on the timing of the spikes to transmit information (Masquelier and Thorpe, 2007) in an unsupervised way, i.e., without a priori group assignments. This SNN is then combined with a Support Vector Machine (SVM) for classification in the STDP-based network using the a priori group assignments (Cortes and Vapnik, 1995). To train the STDP-Network, we used the CSNN-simulator (Falez, 2019).

- SuperSpike-based Spiking Neural Network (SuperSpike-Network). This SNN is trained using a nonlinear voltage-based three-factor learning rule capable of training multilayer networks called the SuperSpike (Zenke and Ganguli, 2018), which is a supervised global learning rule similar to deep learning. We used the Norse simulator for our analyses (Pehle and Pedersen, 2021).
All the neural network simulations were conducted on the cluster "grouille" of the Grid'5000 test bed (Balouek et al., 2013) using two Nvidia A100-PCIE-40GB GPUs, an AMD EPYC 7452 32 core CPU (Zen 2, 2 CPUs/node), and 128GB of RAM. For each type of neural network, we averaged the assignment accuracies obtained over the ten replicate runs to gain robust insight into performance.

The training of the neural networks are expected to be better when a large, data-rich training set is used. Because our two analyzed datasets (Tables 3.2 and 3.3) are composed of 428 and 514 original images, respectively, we considered it necessary to augment the data available for ANN training. Therefore, we performed the following data augmentation procedures:

- Rotate the images by a randomized angle between -15 and 15 degrees and keep all copies.

- Randomly choose images that would be flipped from left to right and keep both copies.

- Rescale random images with values between 1 and 1.3 (with 1 being the default scale value).

The total number of images in each dataset was enhanced to >1000 images via data augmentation.

<table><caption>**Table 3.2.** List of images included (prior to augmentation) in each of the classes for the four-class dataset used for analysis with VGG16 with.</caption><thead><tr><th>Samples</th><th>P. chalara</th><th>Podocyrtis sp. cf. P. chalara</th><th>Podocyrtis sp. cf. P. goetheana</th><th>P. goetheana</th><th>Total number of analyzed specimens per sample</th></tr></thead><tbody><tr><td>1260A 6R 4W 68-70cm</td><td>0</td><td>19</td><td>34</td><td>3</td><td>56</td></tr><tr><td>1260A 6R 4W 119-121cm</td><td>0</td><td>13</td><td>2</td><td>1</td><td>16</td></tr><tr><td>1260A 6R 5W 15-17cm</td><td>0</td><td>12</td><td>7</td><td>0</td><td>19</td></tr></tbody></table>
<table><caption>**Table 3.3.** List of images included (prior to augmentation) in each of the classes for the three class three dataset used for analysis with VGG16, STDP- based SNN and SuperSpike-based SNN.</caption><tr><td>1260A 6R 5W 63-65cm</td><td>22</td><td>9</td><td>15</td><td>0</td><td>46</td></tr><tr><td>1260A 6R 5W 87-89 cm</td><td>33</td><td>12</td><td>18</td><td>14</td><td>77</td></tr><tr><td>1260A 6R 6W 20-22 cm</td><td>12</td><td>18</td><td>9</td><td>0</td><td>39</td></tr><tr><td>1260A 6R 6W 57-59 cm</td><td>19</td><td>14</td><td>2</td><td>0</td><td>35</td></tr><tr><td>1260A 7R 1W 22-24 cm</td><td>24</td><td>15</td><td>0</td><td>0</td><td>39</td></tr><tr><td>1260A 7R 1W 69-71 cm</td><td>33</td><td>11</td><td>0</td><td>0</td><td>44</td></tr><tr><td>1260A 7R 1W 121-123 cm</td><td>31</td><td>11</td><td>0</td><td>0</td><td>42</td></tr><tr><td>1260A 7R 2W 19-21 cm</td><td>0</td><td>13</td><td>0</td><td>0</td><td>13</td></tr><tr><td>1260A 8R 3W 65-67cm</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>TOTAL</strong></td><td><strong>174</strong></td><td><strong>148</strong></td><td><strong>87</strong></td><td><strong>18</strong></td><td><strong>427</strong></td></tr></table>

<table><thead><tr><th>Samples</th><th>P. chalara + Podocyrtis<br>sp. cf. P. chalara</th><th>Podocyrtis sp.<br>cf. P. goetheana</th><th>P. goetheana</th><th>Total number of<br>analyzed<br>specimens per<br>sample</th></tr></thead><tbody><tr><td>1259A 17R 1W 54-56cm</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>1259A 18R 1W 53-55cm</td><td>0</td><td>0</td><td>41</td><td>41</td></tr><tr><td>1259A 18R 2W 53-55cm</td><td>0</td><td>0</td><td>36</td><td>36</td></tr><tr><td>1260A 6R 1W 58-60cm</td><td>0</td><td>1</td><td>26</td><td>27</td></tr><tr><td>1260A 6R 4W 68-70cm</td><td>19</td><td>29</td><td>0</td><td>48</td></tr><tr><td>1260A 6R 4W 119-121cm</td><td>13</td><td>2</td><td>1</td><td>16</td></tr><tr><td>1260A 6R 5W 15-17cm</td><td>12</td><td>5</td><td>0</td><td>17</td></tr><tr><td>1260A 6R 5W 63-65cm</td><td>31</td><td>15</td><td>0</td><td>46</td></tr><tr><td>1260A 6R 5W 87-89cm</td><td>43</td><td>28</td><td>0</td><td>71</td></tr><tr><td>1260A 6R 6W 20-22cm</td><td>29</td><td>8</td><td>0</td><td>37</td></tr><tr><td>1260A 6R 6W 57-59cm</td><td>34</td><td>2</td><td>0</td><td>36</td></tr><tr><td>1260A 7R 1W 22-24cm</td><td>39</td><td>0</td><td>0</td><td>39</td></tr><tr><td>1260A 7R 1W 69-71cm</td><td>44</td><td>0</td><td>0</td><td>44</td></tr><tr><td>1260A 7R 1W 121-123cm</td><td>42</td><td>0</td><td>0</td><td>42</td></tr><tr><td>1260A 7R 2W 19-21cm</td><td>13</td><td>0</td><td>0</td><td>13</td></tr><tr><td><strong>TOTAL</strong></td><td></td><td><strong>319</strong></td><td><strong>90</strong></td><td><strong>105</strong></td><td><strong>514</strong></td></tr></tbody></table>
**3.4 Results**

**3.4.1 Morphometrics and linear discriminant analysis**

The LDA performed on the matrix of our 13 morphometric variables, i.e., measurements, pore
counts and ratios, represented >99 % of the variation on the first two axes and clustered
*Podocyrtis goetheana* and *Podocyrtis* sp. cf. *P. goetheana* successfully (Fig. 3.2).
Comparatively, specimens belonging to *P. chalara* were regularly confused with *Podocyrtis* sp.
cf. *P. chalara* and *vice versa*, resulting overall in 73.5 ± 6.1 % (mean ± sd) correct assignments
(Table 3.4). These two latter morphogroups overlap completely on the LDA plot, whereas *P.
Podocyrtis* sp. cf. *P. goetheana* and *P. goetheana* are relatively well-separated from *P. chalara*
and *Podocyrtis* sp. cf. *P. chalara*, although they share limited overlap with each other (Figure
3.2). The first axis of the LDA mainly represents size variations of the second row of abdominal
pores. The morphological variables that contribute the most to discriminate the morphogroups
on the first LDA axis correspond to the maximum length of the second abdominal pore (LP2),
and the maximum length of the second pore of the abdomen/maximum height of the abdomen
(R1).
<figure><img src="image_30.png" /></figure>

**Figure 3.2.** Scatter plot of the two first axes of the Linear Discriminant Analysis (LDA) conducted on the 13 variables that constituted the morphometric data using four *a priori* identified morphogroups (indicated in the legend; ellipses correspond to the 95 % confidence intervals for each morphogroup). Abbreviations: W1: maximum width of the cephalis without the apical horn; W2: maximum width of the thorax; W3: maximum width of the abdomen; H1: maximum height of the cephalis; H2: maximum height of the thorax; H3: maximum height of the abdomen; TL: total length or height of the specimen without the apical horn; LP2: maximum length of the second abdominal pore; NPV: number of abdominal pores aligned vertically; NPH: number of abdominal pores aligned horizontally; R1: maximum length of the second pore of the abdomen/H3; R2: number of abdominal pores aligned vertically/H3; R3: number of abdominal pores aligned horizontally/W3.

Upon considering three morphogroups achieved by lumping *P. chalara* and *Podocyrtis* sp. cf. *P. chalara*, the LDA ordination is highly similar to that obtained in the four-group analysis, with all variation represented on the first two axes (~92.87 % on axis 1; **Figure 3.S1**). Classification results improved substantially, with 94.5 ± 3.1 % of correctly identified
specimens in the three-group LDA (Table 3.S2). Misclassification mainly occurred between *P. goetheana* and *Podocyrtis* sp. cf. *P. goetheana*, occasionally also between *P. chalara* and *Podocyrtis* sp. cf. *P. goetheana*, but never between *P. chalara* and *P. goetheana*.

<table><caption>**Table 3.4.** Average confusion matrix of the *Podocyrtis* morphotypes based on linear discriminant analysis (LDA) performed on the matrix of our 13 morphometric variables (,,i.e., measurements, pore counts and ratios).</caption><caption>LDA results 4-group scenario</caption><thead><tr><th></th><th>P_sp Cf_P_cha_a<br>nd_P_sp Cf_P_goe</th><th>P_cha_and_P_sp Cf_P_cha</th><th>P_cha_and_P_sp Cf_P_goe</th><th>P_goe_and_P_sp Cf_P_cha</th><th>P_goe_and_P_sp Cf_P_goe</th><th>P_goe_a<br>nd_P_cha</th><th>total<br>correc<br>t</th></tr></thead><tbody><tr><th>res_<br>mea<br>n</th><td>0.011162791</td><td>0.204651163</td><td>0.001627907</td><td>0</td><td>0.04744186</td><td>0</td><td><strong>0.7351</strong></td></tr><tr><th>res_<br>sd</th><td>0.013819733</td><td>0.059589621</td><td>0.005963544</td><td>0</td><td>0.024717954</td><td>0</td><td><strong>0.0612</strong></td></tr></tbody></table>

### 3.4.2 Artificial neural networks

#### 3.4.2.1 Classification using the four morphogroups

First, we trained neural networks on the dataset of 428 images attributed to the four morphogroups (Table 3.2 4_classes dataset) using a CNN with a VGG16 architecture. Prior to the training and testing phases, images were manually grouped into four distinct classes (one class per morphogroup). The analysis was run ten times with the pre-processing parameters outlined in Table 3.5, which resulted to an identification accuracy of 54.4 ± 1.7 % (Figure 3.3). These results indicate that, although the network was able to partially identify the differences between the general morphologies of *P. chalara* + *Podocyrtis* sp. cf. *P. chalara* versus those of *P. goetheana* + *Podocyrtis* sp. cf. *P. goetheana*, it was not able to accurately distinguish all four morphogroups from each other.
<table><caption>**Table 3.5.** Summary of the tests performed on the 4_classes and 3_classes datasets using VGG16, STDP-based SNN and SuperSpike-based SNN. Accuracy values are averaged from the results of the respective 10 test runs and indicated as the mean ± one standard deviation.</caption><thead><tr><th>Dataset</th><th>Architecture</th><th>Test runs</th><th>Number of Epochs</th><th>Data pre-processing</th><th>Simulation time h:m:s</th><th>Accuracy (%) ± std mean ± std</th></tr></thead><tbody><tr><td rowspan="2">4_classes</td><td>VGG16</td><td>10</td><td>20</td><td>Image resize to (224, 224) px</td><td>0:31:46</td><td>54.40 ± 1.74</td></tr><tr><td>VGG16</td><td>10</td><td>20</td><td>Image resize to (224, 224) px</td><td>0:17:37</td><td>92.60 ± 0.77</td></tr><tr><td rowspan="2">3_classes</td><td>STDP-Network</td><td>10</td><td>100</td><td>Image resize to (128, 128) px and On-Off filter</td><td>0:05:4343</td><td>90.40 ± 0.4949</td></tr><tr><td>SuperSpike-Network</td><td>10</td><td>20</td><td>Image resize to (128, 128) px</td><td>0:23:26</td><td>84.42 ± 1.36</td></tr></tbody></table>
<figure><img src="image_31.png" /><figcaption>**Figure 3.3.** Example of a confusion matrix obtained from random single run – Analysis of 4_classes dataset with VGG16. Correct assignments were reached in 54.40 % of the cases. The color scale indicates the number of specimens.</figcaption></figure>

### 3.4.2.2 Classification using the three morphogroups supported by LDA

Subsequently, we trained neural networks using a dataset with a priori assignment to the three morphogroups that were recognized by LDA, i.e., *P. chalara* + *Podocyrtis* sp. cf. *P. chalara Podocyrtis* sp. cf. *P. goetheana* and *P. goetheana*. For these analyses we compared the performance of a VGG16 CNN, a STDP-based SNN and a SuperSpike-based SNN using the 3_classes dataset (Table 3.3). The resulting assignment results and average network accuracies
(Figure 3.4) indicate that, under our specified conditions, all neural networks are able to accurately assign specimens to their correct class when three predefined classes are used in combination with a large dataset of images. However, we observed substantial differences in the speed to conclude analyses; thus the STDP-based SNN was the fastest (around 6 minutes) due to the use of the local learning rule for training and one spike per image per neuron principale. Moreover, VGG16 came second with a time of around 17 minutes due to the size of the network and the use of transfer learning and training of only the last layer. Last came the SuperSpike-based SNN with approximately 23 minutes per run because all layers of the network were trained from scratch during the training phase at the start of each run using a global learning rule.
<figure><img src="image_32.png" /><figcaption>**Figure 3.4.** Examples of confusion matrices obtained from random single runs – Analyses of 3_classes dataset with VGG16, STDP-based SNN and SuperSpike-based SNN. Correct assignments were reached in 92.60 %, 90.40 % and 84.42 % of the cases in VGG16, STDP-based SNN and SuperSpike-based SNN, respectively. The color scale indicates the number of specimens.</figcaption></figure>
## 3.5 Discussion

In this study, we examined the morphological variability in Eocene *Podocyrtis* belonging to the anagenetic sequence that starts with *P. chalara* and ends with *P. goetheana*. Specifically, we examined and compared the performance of two analytical approaches in assigning individuals to *a priori* defined morphogroups that were constructed from qualitative observations, i.e., *P. chalara*, *Podocyrtis* sp. cf. *P. chalara*, *Podocyrtis* cf. *P. goetheana* and *P. goetheana*. The first approach involved LDA based on morphometric data, whereas the second was a neural network approach based on automatic image recognition. Both methods gave very similar results, which indicates that both morphometrics and image analysis evaluated shape differences in a highly similar way, suggesting that the results obtained with these two different methods are robust.

Comparing LDA and neural network approaches, assignment performances were comparatively low when four morphogroups were considered in the morphological transition from *P. chalara* to *P. goetheana*. The scatterplot of the LDA (Figure 3.2), indicated a large morphospace overlap between *P. chalara* and *Podocyrtis* sp. cf. *P. chalara*, revealing that the qualitatively observed ‘differences’ were either not sampled in our datasets or that these differences are part of a larger spectrum of morphological variation and not informative to distinguish morphogroups. Given the similarity of LDA and neural networks, based on different datasets, the second hypothesis is more likely; it appears that *P. chalara* and *Podocyrtis* sp. cf. *P. chalara* represent a single highly variable morphogroup. Morphospace overlap is also observed in the LDA between *Podocyrtis* sp. cf. *P. goetheana* and *P. goetheana*, but it is much more limited than between *P. chalara* and *Podocyrtis* sp. cf. *P. chalara*. Finally, *Podocyrtis* sp. cf. *P. goetheana* also overlaps with *P. chalara* and *Podocyrtis* sp. cf. *P. chalara* in morphospace occupation, however this overlap is small. The analyses conducted with the machine learning
approach on four morphogroups confirm the results of LDA, as VGG16 had significant
difficulties in differentiating specimens of *P. chalara* from *Podocyrtis* sp. cf. *P. chalara* and
*vice-versa* using stacked and segmented images, when all four morphogroup classes were pre-
defined. This was also the case for some specimens of *Podocyrtis* sp. cf. *P. goetheana* and *P.
goetheana*, which all resulted in an inferior performance of VGG16 compared to that of the
LDA for the scenario with four morphogroups, i.e., 54.4 ± 1.7 % versus 73.5 ± 6.1 %,
respectively.

When *P. chalara* and *Podocyrtis* sp. cf. *P. chalara* are lumped in the same morphogroup,
resulting in a three-group configuration, assignment probabilities improved strongly both for
LDA and neural networks. On average 94.5 ± 3.1 % of assignments were correct with LDA,
whereas 92.6 ± 0.8 %, 90.4 ± 0.5 % and 84.4 ± 1.4 % of the assignments were correct for
VGG16, STDP-based SNN and SuperSpike-based SNN, respectively. Neural networks, mainly
our CNN can accurately and quickly, assign specimens to the three pre-defined classes using a
large dataset. Whereas the CNN performed highly similarly to LDA, both SNNs we used here
performed less well, as had been documented for other tasks before (Tavanaej et al., 2019).
Runs with the STDP-based SNN ran to completion fastest and given that the accuracy was only
slightly reduced compared to LDA and VGG16, this approach may be preferred for datasets
that require a very long runtime with similar CNNs. The accuracy of the SuperSpike-based
SNN was significantly reduced compared to all the other classification methods that we used.
Further work is required to determine the cause of this underperformance, but the lower
accuracy for the SuperSpike-based SNN is possibly due to the network size, as we
used only using eight layers in our case compared to 16 layers in VGG-16.
As for the performance evaluation of the neural network approach, it is noteworthy that images
were obtained manually for the purpose of our study, but advances in image technology now
allow that much of the image acquisition and preparation procedures (photographing, stacking
and segmentation) to be automated by the use of automatic microscopes and modification of
the AutoRadio_Segmenter plugin's code (Marchant et al., 2020; Tetard et al., 2020). Using
these automated procedures would facilitate the scalability of the entire analysis with larger
datasets. If such automated procedures were to be used, constructing image datasets may
potentially become more time-efficient than the various procedures that are required to develop
a morphometric dataset. Another advantage of using neural network is their quick run time
(Table 3.5.), although both accuracy and run time would increase upon using larger image
datasets. Furthermore, standardizing the rotation and orientation of specimens is essential in
morphometric studies; however, this requirement can be relaxed for neural networks, as in our
case re-rotating and re-orienting were used in the data augmentation process to enlarge input
datasets.

In our study, we evaluated assignment accuracy of LDA and machine learning with neural
networks based on a priori group assignments; however, in the future, it would be useful to
examine morphological variation without considering such assignments, e.g., by using other
ordination techniques and/or by using unsupervised machine learning techniques. These
methods could help in attempts to evaluate whether the three retained morphogroups represent
natural entities, although based on fossil evidence only such assessments are very difficult. We
refrain from such analyses here, as we believe these would be best conducted with a larger set
of material that ideally would cover the total geographic range and the total stratigraphic
interval covered by P. chalara, Podocyrtis sp. cf. P. goetheana and P. goetheana.
Future work could also be focused on trying to evaluate the capacity of VGG16 to accurately
differentiate between the *P. chalara* and *Podocyrtis* sp. cf. *P. chalara* specimens, with an
altered set of images. Indeed, the weighted activation heatmap (Grad-CAM) (Servaraju et al.,
2017) generated from the runs with the 3-class dataset showed that the analytical focus of the
network was centered around the thoracic and abdominal walls of the specimens (Figure 3.S2
in Supplementary material). One could try to develop a dataset including several unstacked and
non-segmented images per specimen, each one with a focus on specific morphological features.
This would allow some images to contain as much detail as possible on abdominal features,
whereas others would focus on other regions (e.g. the thorax) and include blurred features of
the outer walls and backside of the abdomen. We hypothesize that these manipulations could
force the network to focus its recognition capabilities on a larger set of morphological features.
If this hypothesis would be correct, it would also facilitate the data acquisition process by
eliminating the need to stack and segment the images themselves. Alternatively, it is possible
that providing more fragmented information to neural networks would hamper an efficient
learning process, with negative consequences on the accuracy of the following predictions,
somewhat similar to what we observed in the VGG16 evaluation based on the 4-class dataset.

**3.6 Conclusion**

The aim of our work was to study the morphological variability in the anagenetic sequence of
*P. chalara* to *P. goetheana* and to evaluate the performance of recognizing and classifying four
*a priori* identified morphogroups with various machine learning algorithms based on neural
networks that use image data as direct input in comparison to linear discriminant analysis using
morphometric data. Our results demonstrate that LDA and neural networks provide very similar
outcomes, indicating robust performances. With both approaches we encountered difficulties
distinguishing *P. chalara* and *Podocyrtis* sp. cf. *P. chalara*, suggesting that the qualitative basis
on which these morphogroups were recognized is to be revised. For both approaches,
assignment probabilities drastically increased for the scenario where three morphogroups were
recognized, lumping *P. chalara* and *Podocyrtis* sp. cf. *P. chalara*, and thus retaining *P. chalara*
+ *Podocyrtis* sp. cf. *P. chalara*, *Podocyrtis* sp. cf. *P. goetheana* and *P. goetheana*. Further
studies with more comprehensive sampling are required to document the likelihood of
*Podocyrtis* sp. cf. *P. goetheana* representing a separate natural entity, which additionally has
implications for the position of bioevent RP16. However, our results indicate that the
morphometric data that we used for LDA samples the morphological variability in the
anagenetic sequence of *P. chalara* to *P. goetheana* in a comprehensive way. Secondly, neural
network approaches were able to correctly assign most specimens, and therewith to accurately
distinguish the three morphogroups directly from specimen images. These results indicate that
VGG16, STDP-based SNNs, and even SuperSpike-based SNNs are capable of recognizing
morphological variation in images and thus of reliably distinguishing radiolarian
morphogroups, which could fascilitate identification and help with reaching more objective
taxonomic decisions. Furthermore, neural network approaches can be combined with
automated image acquisition and preparation procedures (photographing, stacking and
segmentation) that enable the creation of much larger image databases in a time-efficient
manner. Analyses based on neural network architecture could thus take a fraction of the time
that would be required for a trained taxonomist/(paleo)biologist to create and analyze
quantitative morphometric datasets. In conclusion, neural network approaches based on images
of (paleo)biological specimens may provide promising opportunities to guide more objective
taxonomic decisions.
## Data availability

Microscopic slides are prepared and stored at UMR 8198 – Evo-Eco-Paleo of the University of Lille, France. The datasets (https://doi.org/10.57745/8KBOFP, Pinto et al. 2023) have been archived in the repository of the University of Lille at Recherche Data Gouv. The codes are available at https://archive.softwareheritage.org/browse/directory/cc7d8ef1505299a208adcde597a98d90b0ca47d6/ (https://archive.softwareheritage.org/browse/directory/cc7d8ef1505299a208adcde597a98d90b0ca47d6/ (Elbez, 2023)).

## Acknowledgments

This study was partly funded by the French government through the program “Investissements d'avenir” (I-ULNE SITE/ANR-16-IDEX-0004 ULNE) managed by the National Research Agency. It also received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 847568. It was also supported by UMR 8198 Evo-Eco-Paleo and IRCICA (CNRS and Univ. Lille USR-3380).

A special thanks to Mazdak Fatahi for very helpful discussions regarding machine learning techniques and to Sylvie Regnier for help and advice with the sample preparation.
# PART 2: ARTIFICIAL INTELLIGENCE APPLIED
TO ASSEMBLAGES

**CHAPTER 4: Subchapter 1 - Convolutional neural network**
**application on a new middle Eocene radiolarian dataset**

Veronica Carlsson<fnref n="1" />,<fnref n="2" />*, Taniel Danelian<fnref n="1" />, Martin Tetard<fnref n="3" />, Mathias Meunier<fnref n="1" />, Pierre Boulet<fnref n="2" />,
Philippe Devienne<fnref n="2" />, Sandra Ventalon<fnref n="4" />

<footnote n="1">¹Univ. Lille, CNRS, UMR 8198, Evo-Eco-Paleo, F-59000 Lille, France.</footnote>

<footnote n="2">²Univ. Lille, CNRS, CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, UMR 9189, F-59000 Lille, France.</footnote>

<footnote n="3">³GNS Science, NZ-5040, Lower Hutt, New Zealand.</footnote>

<footnote n="4">⁴Univ. Lille, CNRS, Univ. Littoral Cote d'Opale, UMR 8187, LOG, F-59000 Lille, France.</footnote>

<footnote>* Corresponding author. E-mail address: [veronica.carlsson@univ-lille.fr](mailto:veronica.carlsson@univ-lille.fr)</footnote>
## Author contributions

Veronica Carlsson is the main author and writer of this paper. She formulated the research methodology of this study in discussion with the other authors. In addition, she classified, analyzed, collected, and processed all images, established the taxonomic catalogue, and built up the image datasets.

Taniel Danelian contributed to the formulation of the research methodology and with supervision and guidance of this study. He also contributed to the revision and discussion of the taxonomy and the writing and revision of the manuscript.

Martin Tetard contributed to the formulation of the research methodology and revision of the datasets, as well as to running the training data. He also contributed to the revision of the paper.

Mathias Meunier contributed to the taxonomic revision of the datasets and the writing and reviewing of the taxonomic catalogue.

Pierre Boulet contributed to the supervision of this study and the revision of the manuscript.

Philippe Devienne contributed to the supervision and revision of the manuscript.

## Abstract

A new radiolarian image database was used to train a Convolutional Neural Network (CNN) for automatic image classification. The focus was on 39 commonly occurring nassellarian species, which are important for biostratigraphy.

The database consisted of tropical radiolarian assemblages from 129 middle Eocene samples retrieved from ODP Holes 1258A, 1259A, and 1260A (Demerara Rise). A total of 116
taxonomic classes were established, with 96 classes used for training a ResNet50 CNN. To represent the diverse radiolarian assemblage, some classes were formed by grouping forms based on external morphological criteria. This approach resulted in an 86.6% training accuracy.

A test set of 800 images from new samples obtained from Hole 1260A was used to validate the CNN, achieving a 75.69% accuracy. The focus then shifted to 39 well-known nassellarian species, using a total of 15 932 images from the new samples. The goal was to determine if the targeted species were correctly classified and explore potential real-world applications of the trained CNN.

Different prediction threshold values were experimented with. In most cases, a lower threshold value was preferred to ensure that all species were captured in the correct groups, even if it resulted in lower accuracies within the classes.

**Keywords:** middle Eocene, radiolaria, convolutional neural network, image database, automated identification, image recognition

## 4.1 Introduction

Polycystine radiolaria are microscopic unicellular protists living currently in all modern oceans; they are characterized by an aesthetically pleasing siliceous skeleton known in the fossil record since the Cambrian (Pouille et al., 2011; Aitchison et al., 2017). Their fossil record is thus of much interest for evolutionary studies (i.e., Danelian and Johnson, 2001; Renaudie and Lazarus, 2013; Tetard et al., 2017; Trubovitz et al., 2020). They are usually the only fossils capable of dating siliceous sedimentary sequences (i.e., Danelian et al., 2012; Vrielynck et al., 2003) and are commonly used in paleoceanography (Matsuzaki et al., 2018; Itaki et al., 2020). Due to the
small number of experts, radiolarian taxonomy is less well-elaborated than the one of other microfossil groups, such as foraminifera and nannofossils.

Today, most of the studies involving identification and counting of microfossils, such as radiolarians, are conducted manually and require substantial taxonomic expertise. This process is known to be time-consuming, particularly when microfossils are used for paleoceanography. Moreover, consistency in species classification may be difficult to achieve between different taxonomic experts. Therefore, artificial intelligence (AI) has been introduced to this field to simplify or automate the work done by micropaleontologists, as for example through automatic image recognition and counting. Several applications of CNNs for automatic image recognition were developed over the last 20 years, since the introduction of SYRACO by Dollfus and Beaufort (1999). Nowadays, CNNs are used on various microfossil groups, such as foraminifera (ex. Mitra et al., 2019, Hsiang et al., 2019; Marchant et al., 2020), coccoliths (ex. Dollfus and Beaufort, 1999; Beaufort and Dollfus, 2004), pollen (ex. Bourel et al., 2020), or even radiolarians (Itaki et al., 2020; Renaudie et al., 2018; Tetard et al., 2020).

Sediments recovered from the Demerara Rise (tropical Atlantic Ocean) during the Leg 207 are rich in middle Eocene radiolarians, preserved in a continuous and expanded carbonate sequence. The encountered radiolarian diversity is immense and based on our estimates it accounts for ca. 500 species, many of which are not described yet. Indeed, although Eocene radiolarians have been studied for about 150 years (since Ehrenberg, 1874) and more in depth for the last 50 years (Riedel and Sanfilippo, 1970, 1978), past research was mainly focused on their biostratigraphic applications (see Meunier and Danelian, 2022 and references therein).
Taking advantage of today's technological achievements, our objective was to design a reliable approach to automatically classify middle Eocene tropical radiolarians from Demerara Rise. The main question addressed in this study is whether a CNN can accurately classify 39 commonly observed nassellarian radiolarian species, most of which have an established biostratigraphic significance. We thus trained a CNN on a newly established image database of middle Eocene radiolarians with a focus on some common nassellarian species. To do this we classified every single object appearing on prepared radiolarian slides. We were inspired by a recent similar study, conducted by Tetard et al. (2020), who studied middle Miocene to Quaternary radiolarians from the West Pacific Warm Pool. We also included additional *Podocyrtis* species prepared for an earlier dataset (Carlsson, 2022). Finally, a new set of a small number of samples was imaged to test the consistency of our trained CNN, which was double checked with manual identifications made by a taxonomist.

## 4.2 Materials and methods

### 4.2.1 Core setting and sample preparation

The middle Eocene samples used in this study were collected during ODP Leg 207 from Demerara Rise, situated off the coast of Suriname (Erbacher et al., 2004, Danelian et al. 2005). This dataset includes samples from several cores recovered from sites 1258, 1259 and 1260. It is noteworthy that the middle Eocene sequence at site 1260 is thick and contains radiolarians of an excellent state of preservation (Danelian et al., 2007). The full sample list used in this study may be found in Supplements 4.1, Table S1. The sediment samples consist primarily of nannofossil and foraminifera chalk, but also contain abundant and well-preserved siliceous microfossils, composed essentially of radiolarians (Danelian et al. 2007, Meunier and Danelian, 2022), as well as diatoms (Danelian et al. 2007, Renaudie et al. 2010).
Sediment samples from ODP holes 1258A, 1259A, and 1260A were first processed to disaggregate organic matter and dissolve carbonates and were then sieved through a 45 µm mesh to remove smaller particles. Thereafter, the samples were prepared using a recent random settling protocol described in Tetard et al. (2020). About 1/3 to ¼ of a microspoon spatula was used from the dried residues, corresponding to approximately 0.2-1.0 g for each sample. Samples were uniformly settled onto 12 mm x 12 mm cover slides using a 3D-printed decanter, as in Tetard et al. (2020), preventing contact between radiolarians and other remaining objects on the slide. The cover slides were allowed to dry overnight before being mounted with NOA81 optical glue. A total of eight different cover slips, all from the same samples, were placed onto one 76 mm x 26 mm glass slide, forming one sample.

Finally, a new set of four samples (see Supplements 4.1, Table S1) was prepared for manually testing the actual accuracy of the trained CNN. An improved cleaning technique was used, which kept only siliceous particles that are larger than 63 µm, and completely removed all clay, calcite and smaller or broken radiolarians (Sanfilippo et al., 1985, Tetard et al., 2020). In fact, most radiolarians are larger, so using a 63 µm sieve will probably just remove smaller broken pieces or radiolarians rather than the radiolarians themselves. At first, about 2-3 cm of raw sediment sample was placed in a plastic beaker. Thereafter 30 ml of 30 % hydrochloric acid was added and left for two hours and until there was no more reaction. Furthermore, 200 ml of distilled water was added to the beaker, which was stirred gently and left to settle for two additional hours. The supernatant was removed and exchanged with 30 ml of 10 % hydrogen peroxide and was left to rest for another two hours. The residue was thereafter washed with a 63 µm sieve into a 100 ml beaker. To remove remaining clay particles, the samples were
processed in ultrasonic bath for ten minutes; they were later sieved again with a 63 µm mesh
and collected afterwards in a filter. Next, the residues were dried in an oven at 50 °C and
transferred into a glass vial.

4.2.2 Image collection and processing

All samples were photographed using a Nikon Eclipse Ni automatic microscope equipped with
a Nikon DS-Ri2 microscope camera and Nikon NIS Element software, using a 20 x objective,
allowing a 200 x magnification and 0.36 µm/pixel resolution. The lowest and highest focal
points on the Z-axis were manually determined for each sample since the depth varied
depending on the thickness of the glass, coverslip, optical glue, and individual radiolarian
specimens. For each field of view (FOV), an image was taken at different focal depths, every
10 µm, depending on the minimum and maximum focal points determined for each sample.
The microscope then automatically stacked all images, taken at different focal points for each
FOV, in order to create a composite image. The microscope was programmed to capture small
images in 20 x 20 (400) FOV, covering about 10 x 10 mm out of the 12 x 12 mm available
surface for each individual sample. The individual FOV images were subsequently merged into
one large mosaic image (see Fig. 4.1), which has as a result to lose less images of specimens
located on the edges of FOVs.
<figure><img src="image_33.png" /><figcaption>**Figure 4.** 1) For each sample, 20 × 20 images are automatically photographed in a convolving way and merged into one large “mosaic composed” image, which enables preserving more complete specimens which are not cut in half. 2) The mosaic composed image thereafter went through grayscale conversion. 3) Segmentation of each unique particle into vignettes and image conversion into 8-bit black and white with black background.</figcaption></figure>

The composite mosaic pattern images received from the automatic microscope were first converted from RGB to 8-bit grayscale using Adobe Photoshop to decrease its size. The images were then segmented into ImageJ using the ImageJ BioVoxxel plugin (Brocher, 2022), and a modified script of the Autoradio_Segmenter plugin (Tetard et al., 2020), which enabled each individual particle to become its own individual image, or vignette. For more details, the reader is referred to Tetard et al. (2020).
Finally, we also included additional *Podocyrtis* species images prepared for an earlier dataset (Carlsson, 2022).

### 4.2.3 Taxa selection and dataset

For this study we decided to focus on 39 species (Plates 4.1 and 4.2), which are the most common in the Middle Eocene interval of Demerara Rise and most of which are used in biostratigraphy (Riedel and Sanfilippo, 1970, 1978; Sanfilippo and Nigrini, 1998; Meunier and Danelian 2022).

**Plate 4.1.** Nassellarian radiolarian species commonly occurring in Middle Eocene sediments of Demerara Rise; species names are followed by the ODP site and hole, core, section and sampled level from which it comes from. A) *Dendropyris stylophora* (Ehrenberg, 1874) from 1259A-20R-4 W, 53–55 cm, B) *Elaphospyris didiceros* (Ehrenberg, 1874) group from 1258A-2R-4 W, 55–57 cm, C) *Liriospyris clathrata* (Ehrenberg, 1874) group from 1259A-20R-4 W, 53–55 cm, D) *Rhabdolithis pipa* Ehrenberg 1854 from 1260A-15R-1 W, 55–57 cm, E) *Lophophaena radians* (Ehrenberg, 1874) group from 1259A-16R-2 W, 55–57 cm, F) *Dictyoprora mongolfieri* (Ehrenberg, 1854) from 1260A-6R-2 W, 55–57 cm, G) *Dictyoprora amphora* (Haeckel, 1887) group from 1258A-2R-4 W, 55–57 cm, H) *Rhopalosyringium auriculaleporis* (Clark and Campbell, 1942) from 1260A-14R-6 W, 55–57 cm, I) *Rhopalosyringium biauritum* (Ehrenberg, 1874) from 1260A-12R-3 W, 55–57 cm, J) *Dictyomitra parva* (Kim, 1992) from 1258A-2R-4 W, 55–57 cm, K) *Lithochytris vespertilio* Ehrenberg, 1874 from 1260A-10R-5 W, 55–57 cm, L) *Sethochytris triconiscus* (Haeckel 1887) from 1259A-20R-4 W, 53–55 cm, M) *Lychnocanium babylonis* (Clark and Campbell 1942) group from 1258A-3R-3 W, 56–58 cm, N) *Lychnocanoma bajunensis* (Renz, 1984) from 1258A-2R-4 W, 55–57 cm, O) *Stichopterygium microporum* (Ehrenberg, 1874) from 1260A-8R-4 W, 54–56 cm, P) *Carpocanopsis ornata* (Ehrenberg, 1874) group from 1260A-6R-5 W, 55–57 cm, Q) *Cycladophora spatiosa* (Ehrenberg, 1874) group from 1259A-20R-1 W, 55–57 cm, R) *Anthocyrtis mespilus* (Ehrenberg, 1847) group from 1259A-16R-2 W, 55–57 cm, S) *Zeolithapium mitra* (Ehrenberg, 1874) from 1258A-2R-3 W, 55–57 cm.
<figure><img src="image_34.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>J</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>K</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>L</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>M</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>N</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>O</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>P</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Q</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>R</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>S</figcaption></figure>

<figure><img src="image_52.png" /></figure>

200 µm
**Plate 4.2.** Nassellarian radiolarian species commonly occurring in Middle Eocene sediments of Demerara Rise; species names are followed by the ODP site and hole, core, section and sampled level from which it comes from. A) *Lophocyrtis alauda* (Ehrenberg, 1874) from 1260A-15R-1 W, 55–57 cm, B) *Aphetocyrtis zamenhofi* Meunier and Danelian, 2023 from 1259A-26R-5 W, 54–56 cm, C) *Theocyrtis scolopax* (Ehrenberg, 1874) from 1260A-15R-3 W, 55–57 cm, D) *Phormocyrtis embolum* (Ehrenberg, 1874) from 1258A-2R-4 W, 55–57 cm, E) *Phormocyrtis lazari* Meunier and Danelian, 2023 from 1260A-8R-6 W, 54–56 cm, F) *Podocyrtis (Lampterium) goetheana* (Haeckel, 1887) from 1259A-18R-1 W, 53–55 cm, G) *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo, 1970 from 1260A-6R-CC, 63–177 cm, H) *Podocyrtis (Lampterium) mitra* Ehrenberg group, 1854 from 1260A-9R-1 W, 55–57 cm, I) *Podocyrtis (Lampterium) sinuosa* Ehrenberg, 1874 from 1259A-15R-1 W, 55–57 cm, J) *Podocyrtis papalis* Ehrenberg, 1847 from 1258A-2R-4 W, 55–57 cm, K) *Podocyrtis (Podocyrtoges) ampla* Ehrenberg, 1874 from 1260A-10R-5 W, 55–57 cm, L) *Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel, 1973 from 1259A-16R-1 W, 55–57 cm, M) *Podocyrtis (Podocyrtoges) diamesa* Riedel and Sanfilippo, 1970 from 1259A-26R-3 W, 54–56 cm, N) *Podocyrtis (Lampterium) puellasinensis* Ehrenberg, 1874 from 1259A-20R-4 W, 53–55 cm, O) *Calocyclas hispida* (Ehrenberg, 1874) from 1260A-6R-4 W, 55–57 cm, P) *Thyrsocyrtis (Thyrsocyrtis) rhizodon* Ehrenberg, 1874 from 1260A-6R-CC, 63–177 cm, Q) *Thyrsocyrtis (Pentalocorys) triacantha* (Ehrenberg, 1874) from 1260A-8R-6 W, 55–57 cm, R) *Eusyringium lagena* (Ehrenberg, 1874) from 1259A-25R-2 W, 55–57 cm, S) *Eusyringium fistuligerum* (Ehrenberg, 1874) group from 1259 to 18R-1 W, 53–55 cm, T) *Rhopalocanium ornatum* (Ehrenberg, 1874) from 1259A-22R-1 W, 55–57 cm.
<figure><img src="image_53.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>J</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>K</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>L</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>M</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>N</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>O</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>P</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Q</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>R</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>S</figcaption></figure>

<figure><img src="image_71.png" /><figcaption>T</figcaption></figure>

<figure><img src="image_72.png" /></figure>

200 µm
Synonymy lists are given in the supplementary catalogue (Supplements 4.3), thus allowing the reader to understand the species concept followed in this study. Taxonomic information for all the other radiolarian classes used in the analysis is also presented in there; most of the other radiolarians were grouped in supraspecific taxa, with taxonomic information and some typical forms given in the catalogue (Supplements 4.3).

The ParticleTrieur software version 2.4.10, developed by Marchant et al. (2020), was used to label our dataset. It includes a built-in k-nearest neighbor (KNN) algorithm, which is a machine learning algorithm that can be used for supervising the classification tasks. It identifies the k-nearest training data points or neighbors for a new data point and predicts a label for the new data based on already labeled data. In the context of image classification, the data points correspond to the pixels in the image. Therefore, ParticleTrieur can recognize patterns in the images for individual classes and suggest classification for new or unclassified images, after some classes have already been added in a semi-supervised way but have of course been validated by a human expert.

We managed to build a dataset consisting of 12,217 images out of a total of ca. 50 000 images, distributed in 116 classes, including the 39 important key-classes of well-known nassellarian species (Plates 4.1 and 4.2). Some of the classes consisted of as few as one specimen per class, while others contained up to nearly a thousand images (i.e. the largest class). Classes with fewer than ten specimens were excluded from the CNN training, resulting in only 96 classes to be trained by the model. The taxonomic framework is in many cases classified based on Meunier and Danelian (2022 and 2023) at the species level, and higher taxonomic ranks are classified, mainly based on Suzuki et al. (2021).
### 4.2.4 CNN training

Earlier studies that attempted to compare the accuracy of several CNNs on microfossil assemblages found that Resnet50 usually performed very well for this purpose (Marchant et al., 2020; Tetard et al., 2020; Mitra et al., 2018); we therefore chose to apply this model for this particular study, instead of MobileNet v1 (Howard et al., 2017), which was used previously (Carlsson et al. 2022) in study focusing on eight closely related species of the Eocene genus *Podocyrtis*. ResNet50 is a deep Convolutional Neural Network architecture (He et al., 2016), and is one of the variations of the ResNet (short for "Residual Network") family of models. The idea behind the formation of ResNet50 is to use residual learning to avoid disappearing gradients in very deep neural networks. Because when the networks get deeper, it becomes more difficult to update the weights of the earlier layers through backpropagation and by using residual learning; the network can thus propagate the gradient signal more easily, which improves the training of deeper networks. The weights of ResNet50 have also been pre-trained on a large dataset, for instance ImageNet, which includes millions of labelled images of about 1,000 classes (He et al., 2016). Our training set consisted of 80 % randomized images, chosen for each individual class present in the database, while the remaining 20 % was used for validation.

### 4.2.5 Performance metrics

The CNN training calculates automatically the classification accuracy and recall values based on the labeled dataset; both of them represent different ways of displaying the CNN performance (Fig. 4.2). For instance, if the aim is to detect all specimens belonging to a specific species (high recall value), sacrificing accuracy by including other objects not belonging to that species might be acceptable. This allows for easier tracking of the true abundance of that
specific species, and misclassified objects may be identified and ignored. However, if misclassified objects, which may belong to another important species, end up in another species class, this would lower their recall value. Therefore, it is important to have a high accuracy overall, but when examining individual classes separately, recall value holds a significant importance. Both indices are of great interest for different applications, more focused on biostratigraphy or paleoceanography for example.

<figure><img src="image_73.png" /><figcaption>**Figure 4.2.** A theoretical example showing the importance of a high accuracy and a high recall value for individual classes.</figcaption></figure>

### 4.2.6. Test set to validate the CNN

To validate the consistency of the CNN training and testing from our dataset, we once again estimated how accurately the trained CNN performed and we compared it with a human operator. The neural network training produced a prediction model that was inserted into ParticleTrieur version 3.0.0. A threshold value can be set directly in ParticleTrieur before classifying new images. The threshold value constrains the degree of accuracy desired for an image needed to be classified into a given class. If the probability for an image/specimen to be classified to a specific class is too low, this image will be left unclassified.
We randomly let the ParticleTrieur software pick 200 images selected from four new samples (800 in total), which were unlabeled and contained all particles segmented from composed images, from ODP site 1260, coming from different intervals within those used initially to train the CNN, which were 1260A-6R-3W, 18-20 cm; 1260A-8R-5W, 70-72 cm; 1260A-13R-5W, 66-68 cm; and 1260A-15R-4W, 69-71 cm. In ParticleTrieur we let the trained CNN identify all of them, using a threshold value of 0.5. We selected this low threshold value since it is better having more images classified, even if that will give a somewhat lower accuracy, recall, precision to classify more images rather than that they unclassified. We then examined afterwards what was correctly or incorrectly classified.

### 4.2.7 Application on 39 species

With the same four samples, we then this time entered all segmented particles from the entire mosaic composed images covering most parts of the coverslips, resulting in a total of 15 932 images, which were automatically classified with the CNN. Here we focused on the targeted 39 classes representing the selected well-known species. We applied different threshold (1-0.5) values for the pre-trained network and checked how the CNN could recognize these 39 most common nassellarian species. The aim of this test was to try future potential applications.

## 4.3 Results

### 4.3.1 Training of the initial dataset

Our trained CNN obtained 86.6 % in overall accuracy, with 75.6 % in precision/accuracy (Fig. 4.2); the latter measures the ability to avoid false positives and corresponds to the number of specimens classified as a class and also belonging to that class, divided by all specimens classified to that class. Our CNN obtained 78 % of recall, which calculates the ability to detect
all correct classifications, as it corresponds to the number of specimens in a class that were
correctly classified divided by the total number of specimens in that class. The training and
validation iterations are given in Supplements 4.2 figure S1, which confirms that the data is
neither overfit, nor underfit.

<figure><img src="image_74.png" /><figcaption>**Figure 4.3.** Simplified confusion matrix, showing the classification between different classes, with a focus on nassellarian super families. The x-axis on the left shows the true classes while the right axis shows recall values; the y-axis at the bottom shows the predicted classes while the top shows the precision value.</figcaption></figure>
Some of the classes that are visible in the confusion matrix (Fig. 4.2 or Fig. S2 in Supplements 4.2 for a more detailed confusion matrix) show a low individual score (diagonal numbers), mainly due to the low number of available specimens (see number next to the label name), or to a high degree of resemblance between closely related or similar looking species. Most of the 39 important species of nassellarians we focused on were classified with a high accuracy (Fig. S2 in Supplements 4.2).

<table><caption>**Table 4.1.** Precision, recall and F1 score for the 39 most important nassellarian species focused in this study.</caption><thead><tr><th>Species</th><th>Training (#)</th><th>Test (#)</th><th>Precision</th><th>Recall</th><th>F1 score</th></tr></thead><tbody><tr><td>Dendropyris stylophora</td><td>35</td><td>7</td><td>0.71</td><td>0.71</td><td>0.71</td></tr><tr><td>Elaphospyris didiceros group</td><td>295</td><td>59</td><td>0.85</td><td>0.90</td><td>0.88</td></tr><tr><td>Liriospyris clathrata group</td><td>77</td><td>15</td><td>0.78</td><td>0.93</td><td>0.85</td></tr><tr><td>Dictyomitra parva</td><td>229</td><td>46</td><td>0.98</td><td>0.98</td><td>0.98</td></tr><tr><td>Dictyoprora amphora group</td><td>124</td><td>25</td><td>0.75</td><td>0.72</td><td>0.73</td></tr><tr><td>Dictyoprora mongolfieri</td><td>271</td><td>54</td><td>0.89</td><td>0.94</td><td>0.92</td></tr><tr><td>Rhopalosyringium auriculaleporis</td><td>104</td><td>21</td><td>0.84</td><td>0.76</td><td>0.80</td></tr><tr><td>Rhopalosyringium biaurata</td><td>24</td><td>5</td><td>0.83</td><td>1.00</td><td>0.91</td></tr><tr><td>Carpocanopsis ornata group</td><td>21</td><td>4</td><td>0.33</td><td>0.50</td><td>0.40</td></tr><tr><td>Stichopterygium microporum</td><td>64</td><td>13</td><td>0.93</td><td>1.00</td><td>0.96</td></tr><tr><td>Sethochytris triconiscus</td><td>22</td><td>4</td><td>1.00</td><td>1.00</td><td>1.00</td></tr></tbody></table>
<table><thead><tr><th>Species</th><th>Training (#)</th><th>Test (#)</th><th>Precision</th><th>Recall</th><th>F1 score</th></tr></thead><tbody><tr><td>Lithochytris vespertilio</td><td>20</td><td>4</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>Lychnocanoma bajunensis</td><td>103</td><td>21</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>Lychnocanium babylonis group</td><td>56</td><td>11</td><td>0.75</td><td>0.82</td><td>0.78</td></tr><tr><td>Lophophaena radians group</td><td>48</td><td>10</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Rhabdolithis pipa</td><td>28</td><td>6</td><td>0.75</td><td>1.00</td><td>0.86</td></tr><tr><td>Aphetocyrtis zamenhofi</td><td>40</td><td>8</td><td>0.60</td><td>0.75</td><td>0.67</td></tr><tr><td>Lophocyrtis alauda</td><td>17</td><td>3</td><td>0.75</td><td>1.00</td><td>0.86</td></tr><tr><td>Theocyrtis scolopax</td><td>22</td><td>4</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Phormocyrtis embolum</td><td>130</td><td>26</td><td>0.71</td><td>0.92</td><td>0.80</td></tr><tr><td>Phormocyrtis lazari</td><td>68</td><td>14</td><td>0.77</td><td>0.71</td><td>0.74</td></tr><tr><td>Calocyclus hispida</td><td>29</td><td>6</td><td>0.57</td><td>0.67</td><td></td></tr><tr><td>Podocyrtis (Lampterium) chalara</td><td>207</td><td>41</td><td>0.98</td><td>1.00</td><td>0.99</td></tr><tr><td>Podocyrtis (Lampterium) goetheana</td><td>115</td><td>23</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>Podocyrtis (Lampterium) mitra</td><td>184</td><td>37</td><td>0.97</td><td>1.00</td><td>0.99</td></tr><tr><td>Podocyrtis (Lampterium) sinuosa</td><td>93</td><td>19</td><td>1.00</td><td>0.79</td><td>0.88</td></tr><tr><td>Podocyrtis (Podocyrtoges) ampla</td><td>42</td><td>8</td><td>1.00</td><td>0.88</td><td>0.93</td></tr><tr><td>Podocyrtis (Podocyrtoges) diamesa</td><td>62</td><td>12</td><td>0.58</td><td>0.92</td><td>0.71</td></tr><tr><td>Podocyrtis (Podocyrtoges) phyxis</td><td>44</td><td>9</td><td>0.89</td><td>0.89</td><td>0.89</td></tr><tr><td>Podocyrtis (Podocyrtis) papalis</td><td>302</td><td>60</td><td>0.95</td><td>0.83</td><td>0.83</td></tr></tbody></table>
<table><thead><tr><th>Species</th><th>Training (#)</th><th>Test (#)</th><th>Precision</th><th>Recall</th><th>F1 score</th></tr></thead><tbody><tr><td>Podocyrtis (Lampterium) puellasinensis</td><td>24</td><td>5</td><td>0.80</td><td>0.80</td><td>0.80</td></tr><tr><td>Pentalocorys triacantha</td><td>100</td><td>20</td><td>0.78</td><td>0.90</td><td>0.84</td></tr><tr><td>Thyrsocyrtis rhizodon</td><td>43</td><td>9</td><td>1.00</td><td>0.67</td><td>0.80</td></tr><tr><td>Eusyringium fistuligerum group</td><td>38</td><td>8</td><td>0.86</td><td>0.75</td><td>0.80</td></tr><tr><td>Eusyringium lagena</td><td>16</td><td>3</td><td>0.50</td><td>0.67</td><td>0.57</td></tr><tr><td>Rhopalocanium ornatum group</td><td>21</td><td>4</td><td>0.67</td><td>0.67</td><td>0.57</td></tr><tr><td>Zeolithapium mitra</td><td>33</td><td>7</td><td>0.54</td><td>1.00</td><td>0.70</td></tr><tr><td>Anthocyrtis mespilus group</td><td>53</td><td>11</td><td>0.78</td><td>0.64</td><td>0.70</td></tr><tr><td>Cycladophora spatiosa group</td><td>53</td><td>11</td><td>1.00</td><td>0.91</td><td>0.95</td></tr></tbody></table>

### 4.3.2 Performance validation from the test set

By using a threshold value of 0.5 the CNN classification resulted in that 769 images, out of a total of 800, were correctly classified. All classes were individually examined and the precision and recall values were calculated for each detected class (see Supplements 4.4). The CNN could classify these images into 76 different classes, while the human classifier considered that these groups belonged to 63 classes, excluding rare species which could not be classified into a proper class and were therefore referred to the class “others”. Finally, an overall accuracy, recall, precision and F1 score were calculated (see Fig. 4.4).
<figure><img src="image_75.png" /><figcaption>**Figure 4.4.** CNN performance metrics based on a test set consisting of a total of 800 images, which were validated by a human expert.</figcaption></figure>

### 4.3.3 Application on new samples

The manual validation of the classification made by a CNN on the four new samples using a threshold value of 1, always provided a correct interpretation without having any misclassified species, although it was far from detecting all classes and all true specimens for each class. Interestingly, with a threshold value of 0.9, we could in some sense detect almost all classes present in the dataset with a 69-73 % accuracy (see Supplements 4.5) and get some estimates for the number of taxa present in the samples. Some possible misclassifications can be easily reviewed at a later stage. Lowering the threshold values increased indeed the number of truly correct specimens in the right species, but it also increased the number of false classifications (see Supplements 4.5). Regarding the average score of predicted key-species using several threshold values (Table 4.2), the CNN was usually able to correctly identify between 24 to 34 out of the 39 targeted species and also not falsely detect species which do not exist (see Supplements 4.5).
<table><caption>**Table 4.2.** Average prediction results of the 39 key-species and its accuracy for different threshold values along with the total number of images.</caption><thead><tr><th>Threshold value</th><th>Predicted key-species</th><th>Correctly predicted species</th><th>Key</th><th>Accuracy key-species</th><th>Predicted images</th><th>Total amount of images</th></tr></thead><tbody><tr><td>1.00</td><td>77</td><td>77</td><td></td><td>1.00</td><td>1664</td><td>15,932</td></tr><tr><td>0.90</td><td>1144</td><td>784</td><td></td><td>0.69</td><td>11,074</td><td>15,932</td></tr><tr><td>0.80</td><td>1336</td><td>865</td><td></td><td>0.65</td><td>12,438</td><td>15,932</td></tr><tr><td>0.70</td><td>1501</td><td>943</td><td></td><td>0.63</td><td>13,462</td><td>15,932</td></tr><tr><td>0.60</td><td>1653</td><td>998</td><td></td><td>0.60</td><td>14,370</td><td>15,932</td></tr><tr><td>0.50</td><td>1799</td><td>1043</td><td></td><td>0.58</td><td>15,232</td><td>15,932</td></tr></tbody></table>

## 4.4 Discussion

### 4.4.1 Classification

Due to the high radiolarian diversity preserved in the studied samples obtained from the equatorial Atlantic, the correct assignment of every single image to a class may be challenging.

One of the particularities of the studied radiolarian fauna is that it contains a lot of rare and undescribed species. In addition, the current state of the art does not allow confident taxonomic divisions in higher classes, as there are often unclear taxonomic boundaries. A lot of taxa are also morphologically similar to each other, and a lot of similarities sometimes exist between different families, giving us often hard time to find for them a proper affinity and even acceptable taxonomic names. As an example, we may state the confusion of *Carpocanopsis ornata* group with juvenile/broken Nassellaria group B and Pterocorythoid group (see the catalogue in Supplements 4.3 and the confusion matrix in Fig. S2, Supplements 4.2).
There have been challenges in determining the most commonly occurring nassellarian species. We aimed to ensure taxonomic clarity within these groups and only included taxa for which we are very confident for their assignment to that specific class. Consequently, numerous similar-looking species have been excluded from the classified dataset. This is because they did not fit into other groups or we judged that they would be confused with the taxon they most resemble with. However, this may be challenging as we strive to represent as much as possible from these samples, while also collecting new samples for automatic classification. As is the case of an example mentioned above, we encountered many specimens that resemble *Carpocanopsis ornata* group, but which do not always display the discriminatory characters of the species. Those specimens would also always be misidentified as *C. ornata* group and were therefore removed from the dataset. It is possible that these forms may be misidentified as *C. ornata* group in future samples, but they have to be looked upon individually afterwards. The important point is to obtain a dataset that consists of clean *C. ornata* group specimens identified with high confidence, so that there can be a clear reference of what a *C. ornata* group looks like (see the catalogue in Supplements 4.3 and the confusion matrix in Fig. S2, Supplements 4.2).

In addition, difficulties have also been met when we attempted to consistently classify a high number of images. Also, differences in individual specimen orientation and bubble inclusions played a big role in getting the CNN to work and to find the proper classes.

Since all objects appearing on a slide are trained and given a class, some of the included classes may be artificially defined and therefore correspond to taxonomically “bin” classes. We focused mainly on nassellarian radiolarians, trying to include as many classes as possible neatly defined at the species level. Although, for some nassellarian classes presented at higher taxonomic
levels, we were obliged to accept a very large taxonomic concept. Regarding spumellarians, as
the recognition of their inner structures is important, but difficult to detect with computer vision,
identifications are even more challenging.

4.4.2 CNN training and new test set score

It is not a shock that the test accuracy, which randomly selected 800 images from four new
samples, performed less than the test of the 20 % of the labeled dataset, from which 80 % were
used to train the CNN. The accuracy of the test is 75.69 %, whereas the training accuracy is
85.6 %. From our labeled dataset, we have purposely removed a lot of “trash” particles, that
will say particles which are broken fragments of radiolarians, blurry background particles etc.
because if we kept these images, the CNN would rather be overtrained by the thousands of trash
images and perform less. Besides that, rare species which consisted of too few species were left
untrained by the neural network and therefore the 20 % of test did not include that many “trash”
images or any rare species, in contrast to the new test set, which were completely randomly
selected among any kind of particle that had been segmented. This last test was just to confirm
how well the CNN was generally trained. For our last application we tried to just focus on the
39 well known radiolarian species, since it is the radiolarians that are of interest.

4.4.3 Feedback on individual species

By examining the 39 targeted species individually in every single sample, we observed that
some species were easily identified correctly, while others performed poorly during the CNN
training iteration and ended up in different classes. Table 4.3 presents the examined species and
samples, along with their training performance. This arrangement facilitates a better
understanding of the high or low number of correctly predicted species based on the training
performance of the CNN.
<table><caption>**Table 4.3.** Training accuracy, recall and number of correctly identified specimens for each one of the 39 targeted species in this study and sample using a threshold value of 0.5, which is the lowest value we used in the test to identify all species which have an identification correctness equal or higher than 0.5. Hyphens “-” correspond to species not found in the samples (Meunier and Danelian, 2022; Meunier and Danelian, 2023).</caption><thead><tr><th>Species</th><th>Accuracy</th><th>Recall</th><th>1260A<br>6R-3 W,<br>18-20 cm</th><th>1260A<br>8R-5 W,<br>70-72 cm</th><th>1260A<br>13R-<br>5 W,<br>66-<br>68 cm</th><th>1260A<br>15R-<br>4 W,<br>69-<br>71 cm</th></tr></thead><tbody><tr><td>Dendrospyris stylophora</td><td>0.71</td><td>0.71</td><td>1</td><td>4</td><td>1</td><td>0</td></tr><tr><td>Elaphospyris didiceros group</td><td>0.85</td><td>0.90</td><td>16</td><td>27</td><td>10</td><td>48</td></tr><tr><td>Liriospyris clathrata group</td><td>0.78</td><td>0.93</td><td>12</td><td>2</td><td>7</td><td>10</td></tr><tr><td>Dictyomitra parva</td><td>0.98</td><td>0.98</td><td>-</td><td>-</td><td>-</td><td>108</td></tr><tr><td>Dictyoprora mongolfieri</td><td>0.89</td><td>0.94</td><td>36</td><td>9</td><td>38</td><td>14</td></tr><tr><td>Dictyoprora amphora group</td><td>0.75</td><td>0.72</td><td>1</td><td>2</td><td>8</td><td>68</td></tr><tr><td>Rhopalosyringium? auriculaleporis</td><td>0.84</td><td>0.76</td><td>-</td><td>2</td><td>3</td><td>3</td></tr><tr><td>Rhopalosyringium? biaurata</td><td>0.83</td><td>1.00</td><td>-</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Carpocanopsis ornata group</td><td>0.33</td><td>0.50</td><td>12</td><td>0</td><td>1</td><td>-</td></tr><tr><td>Stichopterygium microporum</td><td>0.93</td><td>1.00</td><td>2</td><td>3</td><td>6</td><td>3</td></tr><tr><td>Sethochytris triconiscus</td><td>1.00</td><td>1.00</td><td>0</td><td>10</td><td>-</td><td>-</td></tr><tr><td>Lithochytris vespertilio</td><td>1.00</td><td>1.00</td><td>-</td><td>3</td><td>3</td><td>6</td></tr><tr><td>Lychnocanoma bajunensis</td><td>1.00</td><td>1.00</td><td>46</td><td>25</td><td>23</td><td>10</td></tr><tr><td>Lychnocanium babylonis group</td><td>0.75</td><td>0.82</td><td>-</td><td>5</td><td>14</td><td>8</td></tr><tr><td>Lophophaena radians group</td><td>0.70</td><td>0.70</td><td>2</td><td>0</td><td>2</td><td>-</td></tr></tbody></table>
<table><thead><tr><th>Species</th><th>Accuracy</th><th>Recall</th><th>1260A<br>6R-3 W,<br>18-20 cm</th><th>1260A<br>8R-5 W,<br>70-72 cm</th><th>1260A<br>13R-<br>5 W,<br>66-<br>68 cm</th><th>1260A<br>15R-<br>4 W,<br>69-<br>71 cm</th></tr></thead><tbody><tr><td>Rhabdolithis pipa</td><td>0.75</td><td>1.00</td><td>2</td><td>6</td><td>1</td><td>3</td></tr><tr><td>Lophocyrtis alauda</td><td>0.75</td><td>1.00</td><td>—</td><td>—</td><td>8</td><td>9</td></tr><tr><td>Aphetocyrtis zamenhofi</td><td>0.60</td><td>0.75</td><td>—</td><td>—</td><td>0</td><td>20</td></tr><tr><td>Theocyrtis scolopax</td><td>0.00</td><td>0.00</td><td>—</td><td>—</td><td>2</td><td>1</td></tr><tr><td>Calocyclas hispida</td><td>0.57</td><td>0.67</td><td>4</td><td>12</td><td>0</td><td>0</td></tr><tr><td>Phormocyrtis embolum</td><td>0.71</td><td>0.92</td><td>—</td><td>—</td><td>16</td><td>3</td></tr><tr><td>Phormocyrtis lazari</td><td>0.77</td><td>0.71</td><td>9</td><td>4</td><td>—</td><td>—</td></tr><tr><td>Podocyrtis (Lampterium) chalara</td><td>0.98</td><td>1.00</td><td>7</td><td>20</td><td>—</td><td>—</td></tr><tr><td>Podocyrtis (Lampterium) goetheana</td><td>1.00</td><td>1.00</td><td>2</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Podocyrtis (Lampterium) mitra</td><td>0.97</td><td>1.00</td><td>—</td><td>2</td><td>1</td><td>—</td></tr><tr><td>Podocyrtis (Lampterium) sinuosa</td><td>1.00</td><td>0.79</td><td>—</td><td>—</td><td>1</td><td>2</td></tr><tr><td>Podocyrtis (Podocyrtoges) ampla</td><td>1.00</td><td>0.88</td><td>—</td><td>—</td><td>4</td><td>0</td></tr><tr><td>Podocyrtis (Podocyrtoges) phyxis</td><td>0.89</td><td>0.89</td><td>—</td><td>—</td><td>—</td><td>0</td></tr><tr><td>Podocyrtis (Podocyrtoges) diamesa</td><td>0.58</td><td>0.92</td><td>—</td><td>—</td><td>—</td><td>0</td></tr><tr><td>Podocyrtis (Podocyrtis) papalis</td><td>0.94</td><td>0.83</td><td>8</td><td>6</td><td>1</td><td>6</td></tr><tr><td>Podocyrtis (Lampterium) puellasinensis</td><td>0.80</td><td>0.80</td><td>0</td><td>0</td><td>—</td><td>—</td></tr><tr><td>Thyrsocyrtis rhizodon</td><td>1.00</td><td>0.67</td><td>15</td><td>2</td><td>4</td><td>11</td></tr><tr><td>Pentalocorys triacantha</td><td>0.78</td><td>0.90</td><td>12</td><td>43</td><td>7</td><td>4</td></tr></tbody></table>