**Paleontology in the 21st Century**

Editors

**Mary H. Schweitzer Ferhat Kaya**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Mary H. Schweitzer North Carolina State University USA

Ferhat Kaya University of Helsinki Finland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Biology* (ISSN 2079-7737) (available at: https://www.mdpi.com/journal/biology/special issues/ paleontology in 21st century).

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Cover image courtesy of Mary Higby Schweitzer

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## **Contents**

Was There a Cambrian Explosion on Land? The Case of Arthropod Terrestrialization Reprinted from: *Biology* **2022**, *11*, 1516, doi:10.3390/biology11101516 ................ **187**

#### **Zhonghe Zhou**

### *Editorial* **Paleontology in the 21st Century**

**Mary H. Schweitzer 1,2,3,4**

For much of its 300+ year history, "modern" paleontology has been a descriptive science, firmly housed within geological sciences. The application of rigorous phylogenetic methods [1–3] to extinct organisms was a driver of a movement that began in the 1980s to bring paleontology, and particularly deep time paleontology (i.e., >1 Ma), more firmly into the biological sciences. Paleohistology—the investigation of the microstructure of fossil bones initiated by Enlow [4,5] and expanded upon by De Ricqlés and colleagues—(e.g., [5–7]) also influenced how dinosaurs and other fossils would be studied, as biological organisms rather than as geological oddities. The microscopic examination of ancient bone—even dinosaur bone—has revealed the presence of osteons, osteocyte lacunae, and vascular canals in these ancient specimens. Rather than being obliterated by fossilization processes, the retention of these features in bone recovered from Mesozoic and earlier sediments has allowed us to discern biological information from these once-living organisms, such as comparative growth rates and estimates of ontogenetic stages [7–9]. Phylogenetic and histological analyses forever changed the direction of paleontological studies.

However, the application of most molecular biological tools to elucidate evolutionary processes and timing was, until recently, reserved for extant species. The genomic revolution fostered by the advent of a polymerase chain reaction (PCR) and next gen technologies was limited in application to only living or very recently extinct organisms, and was not rigorously and regularly applied to deep time fossils, Jurassic Park notwithstanding.

Although DNA technologies were more readily accepted and proved useful for archaeological materials (see, e.g., [10–13]), the assumption that original, informative organic components were lost at some point during the transition from the biosphere to the geosphere [14,15], and the proposal of a predictable, and short, half-life for DNA and other biomolecules effectively slowed the application of these methods to all but the most recent fossil materials. What else older fossils may be telling us, and what else was possible to know, were questions that were not widely asked.

This is beginning to change, as illustrated in this Special Issue, because it is widely recognized that 1. some endogenous structures and the molecules comprising them are retained in ancient specimens; 2. morphological studies alone have failed to adequately account for convergence or parallel evolution; and 3. fossils are absolutely necessary to incorporate into any studies seeking to determine the patterns, processes, and timing of evolution deep in the Earth's history.

Just as technology in the life sciences has expanded at a record pace, the application of new technologies to paleontological specimens, though slower in coming, is resulting in an explosion in the type of data recoverable from fossils, as well as the type and age of fossils to which these can be applied. For example, the upper limit of DNA preservation, just 20 years ago, was proposed to be 100,000 years [10], but this limit has been repeatedly pushed back in time, most recently to >2 Ma with the recovery of environmental DNA from Greenland [16]. Protein sequence data, on the other hand, has been reported from 3 Ma

**Citation:** Schweitzer, M.H. Paleontology in the 21st Century. *Biology* **2023**, *12*, 487. https:// doi.org/10.3390/biology12030487

Received: 15 March 2023 Accepted: 19 March 2023 Published: 22 March 2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

eggshells [17] to multi-million-year-old dinosaurs [18,19], and immunological evidence for such preservation, suggested as far back as the 1950s [20,21], is becoming more prevalent.

This Special Issue sheds light on the quantum leap in understanding the Earth's rich biological history over the last 4 billion years, brought about by the application of new technologies to old fossils. It highlights new questions we can ask of the fossil record, and the expansion of fossils we can interrogate to yield robust high-resolution data. The authors within this Issue discuss the rapid development of new (or, new to paleontology) methods, once limited to extant organisms, that are now becoming broadly applied to fossils, including those from deep time.

This Issue contains several broad review articles. Tihelka et al. [22] discuss the possibility that animals, in the form of arthropods, invaded the land several million years before what was previously assumed, yielding a terrestrial "Cambrian explosion" of these widespread and taxonomically diverse invertebrates. This hypothesis is supported by combining molecular clock data with fossil evidence. The discrepancies between molecular clock and fossil data are addressed, and a method combining molecular and morphological data to estimate lineage divergence in a total evidence framework is proposed.

The value of fossils for phylogenetic studies has always been recognized, but the value of fossils for molecular studies has recently, and increasingly, been elucidated by authors contributing here [23] and elsewhere. Torres et al. [24] review the long history of paleoimmunological approaches to paleontology, and apply these methods to fossils from the 1.3 Ma Venta Micena site, whereas Tahoun et al. [25] provide an overview of organic molecules recovered from non-avian dinosaurs and contemporary organisms, including pigments and various proteins. They highlight the effort within the paleontological community to understand the mechanisms of such preservation, contributing to the emerging disciplines of molecular paleontology, and molecular taphonomy/diagenesis.

López-Antoñanzas and colleagues [26] further emphasize the need for incorporating fossil data to better understand evolutionary rates and relationships in their review of new, integrative phylogenetic methods. They stress the need for a unified approach to improve accuracy in modeling evolutionary processes and diversity distributions in deep time, and highlight new methods that combine morphological data from living and extinct groups with available molecular data to achieve more accurate evolutionary syntheses. Some of these include combining geometric morphometric and phylogenetic methods, incorporating stratigraphic data into parsimony analyses, and various statistical methods to calibrate a "morphological clock" that uses morphological data from both extant and extinct species.

Tamborini [27] reviews the changing role of paleobiology between the 20th and 21st centuries, pointing to the necessity of fossils in elucidating "deep time patterns and processes". He contrasts the historical differences between paleontology, a geology-based discipline, and the more biological approaches of paleobiology. He focuses his discussion first on the role of paleocolor as a testable hypothesis, stimulated by integrating more data with more technology; second, the search for endogenous organics in fossil materials that has driven the application of new (to paleontology) technologies, such as high-resolution, high-mass accuracy tandem mass spectrometry to address and characterize organic molecules in fossils; third, the integration of morphology and evolutionary theory in investigations of locomotion and mastication via the new role of robotics in 21st century; fourth, the relationship between evolution as expressed in fossils and the development of living organisms ('evo-devo'), resulting in a broader integration between paleontology and biology; and finally, the examination of both biotic and abiotic factors in shaping organisms through evolution. He ends his review by encouraging a new synthesis of knowledge brought about by new data, new fossils, new technologies, and the deeper integration of these disciplines.

Zhou [28] reviews a century of development in paleontology in China, identifying the influences of foreign collectors shaping the first part of this century-wide overview, and notes their influence of both geology and paleontology in China for decades. He notes

not only the increasing participation of Chinese paleontologists to the discipline, but also the role of Chinese fossils, from early hominins (e.g., Peking Man, *Homo erectus*) to early feathered dinosaurs, in shaping a much broader picture of Earth's history. Native-born Chinese paleontologists greatly expanded the breadth of paleontology in the 20th century and continue this trend today, both within and outside of China. The role of international collaborations, increased funding from government sources, and, of course, the incredibly rich paleontological flora and fauna in Chinese deposits has greatly expanded paleontology of all kinds, and will, no doubt, continue this trend into the foreseeable future.

Monson and colleagues [23] highlight various modern approaches to traditional paleontological questions, pointing out the expansion of data derived from CT imaging (including synchrotron imaging) to achieve 3D reconstruction of fossil data, and nondestructive means to study fossil histology as well. These authors describe how combining quantitative genetics and developmental biology approaches allows us to incorporate genotype:phenotype mapping to address morphological variation and its significance in informing phylogenetic history and the role of selection, and provide a case study using these tools.

The presence of endogenous molecules, including protein, DNA, various pigments, and biomarkers, is becoming an increasingly important aspect of paleontological studies, and new technology continues to push back the time limit for the preservation of these important molecules. New methods are being used to test hypotheses rooted in analyses of DNA. Churchill et al. [29] seeks to test the idea of interbreeding between Neandertal and modern humans in Europe and Asia, proposed in earlier DNA studies [30,31] by comparing facial size and shape parameters that may reflect the expression of Neandertal genes, using morphometric techniques.

A wide array of these new technologies are applied to address biomolecular remnants in bones as young as 350 years [32] and as old as the Carboniferous [33]. Phylogenetically and physiologically informative tissues were probed by synchrotron [34] to support the previous identification of reproductive tissues in dinosaurs [35,36]. Technologies continue to broaden not only the type of questions to be asked, but the type of fossils we can analyze, from coprolites [33], teeth [37], and invertebrates [22,38,39] to dinosaurs [25,34,40–44], mammals [45], and our own lineage [29,32,46].

Finally, taphonomic reconstructions remain an important part of paleobiology, and this Special Issue includes multi-dimensional studies on taphonomy using rare earth element studies (REE) to trace the movement of pore waters through bone during fossilization to elucidate the mechanisms contributing to molecular preservation in various dinosaur bone and other fossils [40–43]. However, the recovery of proteins also requires a better understanding of taphonomic modifications, as noted in [37] and previously discussed in [47].

Actualistic taphonomy experiments inform on the modifications introduced during diagenesis, but also informs on possible preservation conditions, recognizing that Mesozoic conditions were very different than today, and may have facilitated preservation through an elevated microbial response to high atmospheric CO2 [48]. Additionally, although exceptionally preserved tissues have long been the target of molecular studies on fossils, there is clearly more to the story, as illustrated by Colleary et al. [44], as not all exceptional fossils preserve endogenous biomolecules.

This Special Issue briefly reviews these cutting-edge technologies and their applications to fossil data in various case studies, indicating a new 'fossil renaissance' for understanding life on this planet, yielding robust data that may be applied to understanding where we are going by better understanding from where we have come.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

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## *Article* **Keeping 21st Century Paleontology Grounded: Quantitative Genetic Analyses and Ancestral State Reconstruction Re-Emphasize the Essentiality of Fossils**

**Tesla A. Monson 1, Marianne F. Brasil 2,3, Michael C. Mahaney 4, Christopher A. Schmitt 5, Catherine E. Taylor 3 and Leslea J. Hlusko 3,6,\***

**Simple Summary:** Over the last two decades of biological research, our understanding of how genes determine dental development and variation has expanded greatly. Here, we explore how this new knowledge can be applied to the fossil record of cercopithecid monkeys. We compare a traditional paleontological method for assessing dental size variation with measurement approaches derived from quantitative genetics and developmental biology. We find that these new methods for assessing dental variation provide novel insight to the evolution of the cercopithecid monkey dentition, different from the insight provided by traditional size measurements. When we explore the variation of these traits in the cercopithecid fossil record, we find that the variation is outside the range predicted based on extant variation alone. Our 21st century biological approach to paleontology reveals that we have even more to learn from fossils than previously recognized.

**Abstract:** Advances in genetics and developmental biology are revealing the relationship between genotype and dental phenotype (G:P), providing new approaches for how paleontologists assess dental variation in the fossil record. Our aim was to understand how the method of trait definition influences the ability to reconstruct phylogenetic relationships and evolutionary history in the Cercopithecidae, the Linnaean Family of monkeys currently living in Africa and Asia. We compared the two-dimensional assessment of molar size (calculated as the mesiodistal length of the crown multiplied by the buccolingual breadth) to a trait that reflects developmental influences on molar development (the inhibitory cascade, IC) and two traits that reflect the genetic architecture of postcanine tooth size variation (defined through quantitative genetic analyses: MMC and PMM). All traits were significantly influenced by the additive effects of genes and had similarly high heritability estimates. The proportion of covariate effects was greater for two-dimensional size compared to the G:P-defined traits. IC and MMC both showed evidence of selection, suggesting that they result from the same genetic architecture. When compared to the fossil record, Ancestral State Reconstruction using extant taxa consistently underestimated MMC and PMM values, highlighting the necessity of fossil data for understanding evolutionary patterns in these traits. Given that G:P-defined dental traits may provide insight to biological mechanisms that reach far beyond the dentition, this new approach to fossil morphology has the potential to open an entirely new window onto extinct paleobiologies. Without the fossil record, we would not be able to grasp the full range of variation in those biological mechanisms that have existed throughout evolution.

**Citation:** Monson, T.A.; Brasil, M.F.; Mahaney, M.C.; Schmitt, C.A.; Taylor, C.E.; Hlusko, L.J. Keeping 21st Century Paleontology Grounded: Quantitative Genetic Analyses and Ancestral State Reconstruction Re-Emphasize the Essentiality of Fossils. *Biology* **2022**, *11*, 1218. https://doi.org/10.3390/ biology11081218

Academic Editors: Mary H. Schweitzer and Ferhat Kaya

Received: 21 July 2022 Accepted: 10 August 2022 Published: 13 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** primates; Cercopithecidae; monkeys; genotype:phenotype mapping; evolution; dentition; phylogeny

#### **1. Introduction**

The most essential, core moment in paleontology is when someone notices a fossil as something other than a rock and collects it for scientific study. This event is often just a person walking across the landscape, scanning the ground for evidence of past life. While this simple act has been fundamentally the same for generations of paleontologists, the lead-up to that moment and the science that follows have evolved dramatically. The technological advances that have taken us from landline telephones to smartphones have similarly altered how the science of paleontology is conducted. We can see this in the way scientists discover fossil sites. Where fossiliferous sediments were once identified mostly by happenstance, aerial photography, then satellite imagery, and now remote sensing are common tools for field paleontologists [1–3]. As well, our protocols for the collection, inventory, and organization of fossils now rely on fine resolution GIS [4] and remote access to the internet [5].

The laboratory side of the science is also remarkably different from 20th century paleontology. Fossils are now imaged by laser scanners as well as through photography [6,7]. Quantification of those scanned surfaces can be performed in three-dimensions with thousands of points, opening the door for new analytical approaches to morphological variation [8,9] and enabling the digital reconstruction of crushed fossils [10]. With the application of computed tomography (CT), paleontologists can more readily study internal bony structures [11,12], giving them the ability to reconstruct soft-tissue anatomies [13,14]. CT scans have become an essential tool in the description of new fossils [15]. With a synchrotron, we can even see fossilized histology without mechanically damaging specimens [16,17]. Advances in geochemistry provide new insight into the evolution of dietary niches [18–21] and life history [22], not to mention the ability to geologically date fossils [23]. As well, of course, advances in artificial intelligence and machine learning have forever changed taphonomy [24,25], approaches to fieldwork [26,27], and trait analysis [28–30].

Paleontologists have also incorporated new knowledge from biology and genomics. As genomic sequencing became increasingly possible for a wide range of organisms, paleontologists began to combine morphological evidence from fossils with genomic data to reconstruct phylogenetic relationships [31–33].

Alongside the genomic revolution, there is another discipline in biology with significant implications for paleontology: elucidating the relationship between genotype and phenotype, often referred to as genotype:phenotype (G:P)-mapping. The insight that comes from G:P-mapping will fundamentally alter how we approach fossil morphologies in the 21st century and, consequently, improve our knowledge of the evolutionary past. To demonstrate this point, we investigated the insight that G:P-mapped dental traits bring to the African fossil record of monkeys (Primates: Cercopithecidae). We first used quantitative genetic analyses to assess the heritability and covariate effects on traditional measurements of tooth size and two types of G:P-mapped traits, one derived from developmental biology and the other from quantitative genetic analyses. We then compared how these traits vary across extant cercopithecids to test Hypothesis 1: G:P-mapped dental traits can provide evidence of phylogenetic history and selection, and therefore, are useful in paleontological investigations. We then focused on the traits defined through our quantitative genetic approach and explored how they vary in the fossil record to test Hypothesis 2: G:P-mapped traits reveal a range of morphological variation that cannot be predicted solely through extant variation.

#### **2. Background: Traditional and G:P-Mapped Dental Traits**

Paleontologists have long relied on the size of the postcanine teeth (especially the molars) to serve as a proxy for body size, to provide essential insight into taxonomy, and to observe patterns of evolution [34–37]. Tooth size is traditionally defined as the twodimensional occlusal area of the crown, calculated by multiplying the mesiodistal length by the buccolingual breadth (Figure 1C). This trait has long been, and still is, an essential trait in mammalian paleontology.

**Figure 1.** Illustration of the four maxillary dental traits investigated in this analysis. Each panel shows the right maxillary occlusal view of two extremes for one of the traits. Mesial is to the top, distal to the bottom, lingual to the right, and buccal to the left. The axis at the bottom of the figure orients the reader to how the morphology varies according to low and high values of the trait. (Panels (**A**,**B**)) demonstrate the two traits defined through quantitative genetic analyses, ratios that reflect the relative size variation between the premolar and molar genetic modules (PMM; panel (**A**)) and the relative sizes of the molars within the molar module (MMC; panel (**B**)). (Panel (**C**)) shows the traditional method for studying molar size variation within paleontology, by calculating a two-dimensional area of the occlusal view of the crown. (Panel (**D**)) shows the "inhibitory cascade" (IC) trait, defined through developmental gene expression studies of mice. See text for more detailed descriptions.

Over the last couple of decades, technological advances in the biological sciences have enabled scientists to probe the genetic influences on tooth size variation. There are two main avenues for G:P-mapping of dental variation: quantitative genetics and developmental biology. Quantitative genetic analyses approach the G:P-map through phenotypic variation, investigating how anatomical variation is inherited through family lineages. So long as the family structure within a population is known, any taxon can be studied, including large-bodied and long-lived animals such as primates. Because quantitative genetics reveals the genetic contributions to phenotypic variation within a population, this approach is particularly informative for Neogene paleontology, as population-level variation is most applicable to micro-evolutionary questions [38,39]. In contrast, developmental approaches involve the manipulation of embryogenesis and organogenesis to gain insight into the formation of the dentition from a fertilized egg. Consequently, experimental developmental biology is limited to animals that are amenable to being raised in a laboratory setting, who have short generation times, and/or for whom organs can be grown in culture, such as mice.

While there is a deep history of quantitative genetic research on the dentition [40], results from recent analyses have clarified that individual teeth are not genetically or developmentally independent structures, and that different aspects of a tooth are underlain by different genetic and non-genetic influences. For example, minor shape variants on the crown are genetically independent of tooth size [41]. Looking along the dental arcade, we see that the size of the incisors is genetically independent from the size of the premolars and molars (in baboons [42]; and macaques [43]; with some suggestive evidence in humans [44,45]; but see tamarins [46,47], and a different study on humans [48]), yet there is significant pleiotropy between postcanine teeth [42,43,46–48]. Evidence of pleiotropy indicates a genetic correlation, meaning that a significant proportion of the residual phenotypic variance in the two traits is due to the shared additive effects of the same gene or set of genes. Thus, evidence of pleiotropy helps elucidate the underlying genetic architecture. Shared genetic effects are not just limited to within the dentition. In baboons, for example, we also discovered that molar width is genetically correlated with body size (with more than 20% of the additive genetic covariance between these traits estimated to be due to the same gene or set of genes), but in surprising contrast, molar length is not [49]. While this exact correlation has not yet been explored in other primates, variation in crown area for humans has a positive correlation with the length of the dental arch, and a negative correlation with arch width, suggesting that tooth area and size dimensions within human dentitions are similarly not uniform [48]. Based on this genetic evidence, we now know that variation in the 2D occlusal area (as studied by paleontologists) reflects a range of underlying genetic effects related to body size and sex in addition to the genetic effects that pattern dental variation.

In order to make this quantitative genetic evidence translatable to paleontological research, Hlusko and colleagues [38] developed two dental traits that reflect the genetic architecture of the baboon dentition: the molar module component (MMC) and the premolarmolar module (PMM). Both traits are based on our quantitative genetic analyses of baboon mandibular dental variation. These analyses revealed that the mesiodistal lengths of the first, second, and third molars share a genetic correlation that is essentially 100%, indicating that first, second, and third molars are, genetically speaking, not the separate, independent structures that anatomists have long viewed them to be, but rather, one organ [42,50,51]. Consequently, the relative mesiodistal lengths of the first, second, and third molars represent components within one genetic module. As mentioned previously, molar buccolingual width has significant pleiotropic effects on body size [49]. Therefore, Hlusko et al. [38] proposed the ratio of the mesiodistal length of the third molar divided by the mesiodistal length of the first molar as a trait (MMC) that captures the genetic variation influencing tooth size variation within the molar module without the genetic effects that also influence body size (Figure 1B). Consequently, MMC is a more direct reflection of the underlying genetic architecture influencing molar size variation than two-dimensional crown area

(length × width) because 2-dimensional crown area results from a combination of genetic effects that include those that influence body size.

We also defined PMM as a ratio that reflects the genetic correlation between the size of the fourth premolar relative to the size of the molar module [38]. Previous analyses demonstrated that the mesiodistal length of the fourth premolar has an overlapping, but not complete genetic correlation with the mesiodistal length of the molars [42,50,51]. PMM is the mesiodistal length of the second molar divided by the mesiodistal length of the fourth premolar (Figure 1A). As with MMC, we focused on the mesiodistal lengths in order to avoid conflating the genetic effects on body size with those that influence dental patterning.

The mandibular versions of MMC and PMM were first identified for cercopithecid monkeys and then expanded to apes, revealing an episode of selection during the Late Miocene [38]. While we do not yet know the genetic mechanisms that underlie PMM and MMC, we do know that these two ratios reflect a genetic architecture that does not simultaneously influence body size or sex, and that appears to primarily influence variation in the relative sizes of teeth in the postcanine dentition of catarrhine primates [38,52] and many other mammals [53,54].

The influence of developmental mechanisms on two-dimensional molar size variation has also been explored. Kavanagh and colleagues [55] reported evidence of an inhibitory cascade within the molar teeth of mice that can explain variation in the relative sizes of the first, second, and third molars. Through experimental manipulation of cultured tooth germs, they found that the timing of first molar initiation influences the initiation time and ultimate size of the second and third molars. For example, the removal of the first molar bud led to earlier initiation of the second and third molars, and these later-forming teeth grew larger. Kavanagh and colleagues [55] observed that across murine rodents, the size of the second molar always accounts for approximately one-third of the two-dimensional size of the molar row in occlusal view, and that the relative sizes of the first and third molar vary around this. From these observations, they [55] proposed that evolution follows this rule of one-third, and that first and third molar size can be predicted from each other. This model is referred to as the inhibitory cascade (IC) model. The model fits well with the phenotypic variation observed across murines [55] and has been supported in a range of other mammals (e.g., early mammaliaforms [56]; kangaroos [57]; many but not all South American ungulates [58]; and many but not all rodents [59]). However, the IC model does not fit the patterns of variation observed for anthropoid primates [60,61], humans [62], and some earlier hominids [63].

For Hypothesis 1, we explore both types of G:P-mapped traits in the maxillary dentitions, the IC (from developmental biology), and the MMC and PMM (from quantitative genetics). For Hypothesis 2, we focus on the quantitative genetics-derived traits, complementing the previously published investigation of the mandibular versions of PMM and MMC with the maxillary analyses.

#### **3. Materials and Methods**

Our analyses rely on dental linear metrics from three different samples described in detail in the following paragraphs. The quantitative genetic analyses were performed on data from 611 individuals within a captive pedigreed population of *Papio hamadryas* baboons. The extant, neontological analyses were performed using data from 825 museum skeletal specimens representing 13 genera within Cercopithecidae. Finally, we augmented the data we collected from museum specimens with data culled from the published scientific literature to create a fossil dataset of 1,436 individuals from 17 genera representing the last 20 million years of cercopithecid evolution in Africa.

**Sample 1, quantitative genetics:** The baboons from which dental data used in our quantitative genetic analyses were obtained are members of a large, six-generation pedigree (*n* = 2426), developed and maintained at the Southwest National Primate Research Center (SNPRC) at the Texas Biomedical Research Institute (Texas Biomed) in San Antonio, Texas. The pedigree was genetically managed to minimize inbreeding, and ascertainment of

animals for this study was random with respect to phenotype. We analyzed linear crown metric data for the maxillary fourth premolar and first, second, and third molars obtained from 611 members of the single, large, six-generation pedigree. The female to male sex ratio was approximately 2:1 and the mean age of the sample was approximately 16 years, with ages ranging from 8 to 32 years. All procedures involving animals were reviewed and approved by Texas Biomed's Institutional Animal Care and Use Committee. SNPRC facilities and animal use programs at Texas Biomed are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International, comply with all National Institutes of Health and U.S. Department of Agriculture guidelines, and are directed by Doctors of Veterinary Medicine.

**Sample 2, extant variation:** Our comparative sample of extant taxa includes 825 individuals (Table 1). Most of the extant comparative data were collected by the authors and have been included in previously published research [64]. This dataset builds on the published dataset [65].

**Table 1.** Taxonomic composition of the extant comparative dataset.

**Sample 3, extinct variation:** Our comparative sample of fossil taxa includes 1436 individuals (Table 2). Fossil data include measurements collected by the authors, culled from published sources, and downloaded from PRImate Morphometrics Online (PRIMO). Data sources for each sample are specified in Table 2.

**Table 2.** Taxonomic composition of the fossil comparative dataset \*.

\* Data sources: 1 [66]; 2 [67]; 3 (Authors measured at the National Museum of Ethiopia); 4 (Authors measured at the Ditsong Museum of Natural History); 5 [68]; 6 (Authors measured at the University of California Museum of Paleontology); 7 (Authors measured at University of the Witswatersrand); 8 [69]; 9 (PRIMO); 10 [70]; 11 [71]; 12 [72]; 13 [73]; 14 [74]; 15 [75].

**Data collection:** Tooth dimensions for the SNPRC baboons are described in Hlusko et al. [76]. For the other two samples, mesiodistal length and buccolingual breadth measurements were collected from the maxillary fourth premolar (P4) and the three maxillary molars (M1, M2, and M3) for each individual, for both left and right sides, following standard protocols (see [64]). For the measurements collected by our research team, we did not account for interstitial wear. For the data culled from other publications, we refer to those publications, noting that some authors do not explicitly state how they measured mesiodistal length on teeth with significant interstitial wear. We used these two linear measurements, mesiodistal length (L) and buccolingual breadth (W) (see inset of Figure 1), to calculate 2-dimensional occlusal area, MMC, PMM, and the IC (see Figure 1 for equations).

**Abbreviations:** Premolars are abbreviated as P, molars as M. The letter for the tooth (P or M) is followed by a number indicating tooth position. For example, M2 refers to the second molar. We are primarily focused on a discussion of maxillary molars in this manuscript. We specifically indicate if a measurement or tooth is from the mandibular dental arch in the text rather than through abbreviations.

**Overview:** In order to test Hypothesis 1, we first established that a significant proportion of the phenotypic variation in all of the six traits is attributable to the effects of genes, i.e., that all the traits are heritable. To do this, we estimated the heritability of the traits in the SNPRC baboons. We then assessed the variation of all six traits across a sample of extant cercopithecid monkeys and considered how they vary within a phylogenetic context through a phylogenetic ANOVA. We followed the ANOVA with an analysis to test whether the traits are phylogenetically conserved or show evidence of selection. For the test of Hypothesis 2, we focused on the two traits derived from quantitative genetics: PMM and MMC. We first reconstructed ancestral states (ASR) based on the phylogenetic relationships of the extant genera analyzed for Hypothesis 1. We then compared the ASR trait values derived from the extant taxa to the PMM and MMC values observed in the fossil record.

**Quantitative genetic analyses:** We conducted statistical genetic analyses using a maximum likelihood-based variance decomposition approach implemented in the computer package SOLAR ([77]; v 8.1.1, www.solar-eclipse-genetics.org). This approach partitions the observed covariance between individuals into genetic and environmental components. The variance components are additive, with the phenotypic variance (*σ*2 *P*) being the sum of the genetic (σ2 *G*) and environment (σ2 *E*) variances. Estimates of heritability (*h*2), the proportion of the phenotypic variance attributable to additive genetic effects, were obtained as:

$$h^2 = \sigma\_G^2 / \sigma\_P^2$$

Unless otherwise noted, all quantitative genetic analyses were conducted following inverse gaussian normalization of the residuals (trait values were adjusted for the mean effects of sex and/or age, the latter a rough proxy for wear, if significant). Significance of the maximum-likelihood estimates for heritability and other parameters was assessed by means of likelihood ratio tests [78]. The maximum likelihood for a general model in which all parameters were estimated was compared to that for restricted models in which the value of the parameter to be tested was held constant (value dependent on null hypothesis). Twice the difference in the log-likelihoods of the two models compared is distributed asymptotically approximately as either a 1/2:1/2 mixture of χ2 with a point mass at zero for tests of parameters such as h2 for which a fixed value of zero in a restricted model is at a boundary of the parameter space or a χ2 variate for tests of covariates for which zero is not a boundary value [79]. In both cases, degrees of freedom are obtained as the difference in the number of estimated parameters in the two models [79]. However, in tests of parameters such as h2, where values may be fixed at a boundary of their parameter space in the null model, the appropriate significance level is obtained by halving the *p*-value [80].

**Descriptive statistics:** Statistical analyses were completed in the R statistical environment v3.2.2 [81]. We first calculated univariate descriptive statistics for the two-dimensional areas, IC, MMC, and PMM values for all taxa included in the study, using built-in functions

in R. Kurtosis was calculated using the *moments* package in R [82]. We visualized the distribution of the MMC and PMM traits across taxa in R using the package *ggplot2* (v1.0.1; [83]).

**Phylogenetic ANOVA:** We conducted a phylogenetic ANOVA to investigate variation across cercopithecid genera using the aov.phylo function in *geiger* [84]. The phylogenetic ANOVA uses average species data to compare traits across genera. Analyses were run on left side maxillary data. When no left side data were available, the right side was included. All dental areas were geometric mean size-corrected prior to analysis. All other dental traits are unit-free ratios.

**Phylogenetic analyses:** For all phylogenetic analyses, we used a consensus molecular chronogram based on a Bayesian phylogenetic analysis of genetic data downloaded from the 10kTrees v.3 database, built using data from six autosomal genes and 11 mitochondrial genes sampled from GenBank [85]. *Presbytis rubicunda* is not available in the 10kTrees database, and so we added this taxon manually to the phylogeny in R using a branch length split age of 1.3 million years from *Presbytis melalophos* [38,86].

**Test of phylogenetic signal and selection:** We tested the phylogenetic signal of the dental traits with a Blomberg's K analysis using phylosignal in *picante* [87]. Blomberg's K tests whether a trait is present in closely related taxa more frequently than would be expected by Brownian motion [88]. The K value for a trait can be either less than 1, equal to 1, or greater than 1. A K value > 1 is generally interpreted as more phylogenetically conserved than expected under neutral Brownian motion, while a K value of 1 generally indicates Brownian evolution of the trait under drift. In contrast, K < 1 is generally interpreted as a trait that is phylogenetically conserved, although less so than expected under a Brownian model, suggesting that selection pressures may be influencing the distribution of the trait in ways that deviate from the pattern expected based on phylogeny (with K = 0 implying that a trait varies in a pattern completely unrelated to phylogeny). However, heterogeneous rates of genetic drift or rapid divergence between species can also result in low K values [88,89]. We used summary trait values for each species and compared average species values across genera.

**Ancestral state reconstruction:** To investigate how dental traits have evolved in cercopithecids, we generated a series of ancestral state reconstructions (ASR) using contMap in *phytools* [90], which maps continuous variables across a phylogeny. We quantified the estimated values at internal nodes using fastAnc in *phytools* [90], a function that generates maximum likelihood ancestral states for continuous traits.

#### **4. Results**

#### *4.1. Test of Hypothesis 1: G:P-Mapped Dental Traits Can Provide Evidence of Phylogeny and Selection*

The results of the quantitative genetic analyses are presented in Table 3. Statistically significant residual h2 estimates, ranging from 0.611 to 0.728, were obtained for five of six two-dimensional areas, two on the left side and three on the right. Both sex and age exerted significant mean effects on the two left side 2-dimensional areas, while only sex influenced the three right side traits. These covariate effects were substantive, accounting for approximately 28% to 51% of the total phenotypic variance in these five 2-dimensional areas. These same analyses returned significant h2 estimates (range: 0.491–0.604) for three of the six G:P-mapped traits: right IC, and right and left PMM, with sex being the lone significant covariate, accounting for approximately 2% to 9% of their total phenotypic variance.

The analyses did not return statistically significant heritability estimates for four phenotypes, three on the left side of the arch (M3 2D area, IC, MMC) and one on the right (MMC). Derivation of these traits was based on data from comparatively small numbers of animals: i.e., only 140 to 221 individuals of the more than 600 pedigreed baboons from which data were obtained for this study.

**Extant variation descriptive statistics:** Univariate statistics for the two-dimensional areas of M1, M2, and M3, and the G:P-mapped traits (IC, PMM, and MMC) are reported in Tables 4 and 5. These are based on the phenotypic observations of the taxa listed in Table 1. See Supplementary Table S1 for more detailed descriptive statistics (Table S1).

**Table 3.** Residual heritability estimates for the three types of maxillary dental traits: two-dimensional area, IC, MMC, and PMM \*.

\* L = left; R = right; 2D = 2-dimensional; M1, 2 or 3 = first, second, or third molar; IC = inhibitory cascade trait; MMC = molar module component ratio; PMM = premolar-molar module ratio. \* indicates sex only is a significant covariate. \*\* indicates sex and age are significant covariates. Shaded rows are statistically non-significant at *p* < 0.05.

**Table 4.** Descriptive statistics for the two-dimensional area traits \*.

\* M1, M2, and M3 areas refer to the two-dimensional area of the tooth in occlusal view, calculated as the mesiodistal length multiplied by the buccolingual breadth. See text for details and definitions. StDv = standard deviation. See Supplementary Table S1 for more extensive descriptive statistics.

**Table 5.** Descriptive statistics for the Genotype:Phenotype (G:P)-mapped traits \*.

\* MMC = molar module component; PMM = premolar-molar module; IC = inhibitory cascade. See text for details and definitions. StDv = standard deviation. See Supplementary Table S1 for more extensive descriptive statistics.

**Phylogenetic ANOVA:** Results from the phylogenetic ANOVA are presented in Table 6. The summary *p*-values indicate that all six traits differ significantly across the genera included in the analyses. The *p*-values for each genus are also presented. For two-dimensional areas, *Nasalis*, *Colobus*, *Macaca*, *Lophocebus*, and *Erythrocebus* are not different from the pooled value of the trait across all the extant genera. *Piliocolobus* is only statistically different for the M2. *Chlorocebus* is only statistically different for the M2 and M3 two-dimensional areas. IC and MMC results are identical, demonstrating that *Cercopithecus*, *Mandrillus*, *Papio*, and *Theropithecus* are statistically significantly different from the pooled values of IC and MMC. PMM differentiates most of the papionins (*Macaca*, *Papio*, and *Theropithecus*) as well as the colobine *Nasalis* from the other genera.

**Table 6.** Phylogenetic ANOVA results for extant genera \*.

\* M1, M2, M3 refer to the first, second, and third molars. 2D refers to the two-dimensional area of the tooth crown in occlusal view, calculated as the mesiodistal length multiplied by the buccolingual breadth. IC is the 2-d area of the M3 divided by the 2D area of the M1. MMC is the mesiodistal length of the M3 divided by the mesiodistal length of the M1. PMM is the mesiodistal length of the M2 divided by the mesiodistal length of the P4 (fourth premolar). All area traits were geometric mean size-corrected before analysis. \* indicates significance at *p* < 0.05. \*\* indicates significance at *p* < 0.01. \*\*\* indicates significance at *p* < 0.001.

**Phylogenetic signal:** Blomberg's K-values for the six traits are reported in Table 7. These all range between 0.625 and 0.673. Statistically non-significant *p*-values indicate that the trait is evolving neutrally under Brownian motion. IC is marginally significant at the *p* = 0.05 level, and therefore may indicate that IC variation observed across these extant taxa is the result of selection. MMC is statistically significant at the *p* = 0.05 level, providing a clear indication that selection has likely been operating on the relative mesiodistal lengths of the molars. Blomberg's K is a conservative test that is sensitive to sample size [88]. Additionally, variation in sample sizes across taxa, as well as variation in sample source populations within taxa, have been demonstrated to skew mean trait values used in these analyses, which can in turn skew results [91]. Sampling more extensively within sparsely sampled taxa, and across a broader range of primate taxa, may reveal stronger phylogenetic signal for these traits.

**Table 7.** Blomberg's K for the dental traits \*.

\* M1A, M2A, and M3A = two-dimensional area estimates for first, second, and third maxillary molars; MMC = molar module component; PMM = premolar-molar module; IC = inhibitory cascade. See text for trait definitions. Statistically significant estimates are in bold text. K-values greater than 1 indicate a strong phylogenetic signal. Non-significant *p*-values are interpreted as evolution under neutral genetic drift. For K-values that are significant at *p* < 0.05, the trait is interpreted to show evidence of selection.

#### *4.2. Test of Hypothesis 2: G:P-Mapped Traits Reveal a Range of Morphological Variation That Cannot Be Predicted Solely through Extant Variation*

**Ancestral State Reconstruction (ASR):** ASR estimates based on the extant genera listed in Table 1 are presented in Table 8, with nodes defined on the molecular phylogeny shown in Figure 2.

**Table 8.** Comparison of trait values from the Ancestral State Reconstruction (ASR) and Possible Fossil Representatives \*.

\* Ma = million years ago; ka = thousand years ago; MMC and PMM are defined in the text; Molecular divergence estimates: Node 20 [92]; Node 28–31 [93]; Node 35 [94]. Geological dates for the fossils: *Victoriapithecus*, *Parapapio*, *Paracolobus*, *Cercopithecoides* [95]; *Procercocebus* [96]; *Soromandrillus* [97]; *Pliopapio* [66]; *Kuseracolobus* [73]; *Libypithecus* [98]; cf. *Chlorocebus* (authors, unpublished data); *Colobus* (authors, unpublished data).

**Figure 2.** Molecular phylogeny of the extant cercopithecid genera included in this analysis with ASR nodes indicated. See Table 8 for ASR MMC and ASR PMM estimates.

**Comparison to fossil data:** In order to compare the ASR trait values to the anatomical variation observed in the fossil record, we compiled data for 17 fossil genera (Table 2) that could possibly be a fossil representative for one of the ASR nodes (Table 8). We include the molecular divergence date estimates that correspond to each node in the phylogeny. Next to these data, we list the possible fossil representative genus, along with the MMC and PMM values associated with that genus and the associated geological age range. Note that some fossil genera are potentially associated with more than one node. We present these data visually in Figure 3, along with the extant data for comparison. The averages for the fossil genera are indicated with a skull icon. Each fossil data point is linked with a double-ended arrow to the ASR node/estimate it may potentially represent, highlighting the difference between them. For both the PMM and MMC, the ASR estimates are usually lower than the values observed in the fossils. We present the absolute value of the difference between the ASR trait estimate and the fossil trait in Figure 4. Absolute value of the average difference between ASR MMC and fossil MMC is 0.066. Absolute value of the average difference between ASR PMM and fossil PMM is 0.162. At all of the time points represented by these data, the difference between the ASR value and the fossil value is most distinct for PMM.

**Figure 3.** Box and whisker plots showing the range of variation for PMM and MMC within the sampled extant genera (labeled at the bottom of the figure). The genera are color-coded, with tribe Cercopithecini in gold, tribe Papionini in blue, and the subfamily Colobinae in purple. In addition to the extant data, we plot trait estimates for the Ancestral State Reconstruction (ASR) nodes as horizontal dotted lines, labeled with N and the number of the node. The possible fossil representatives for these nodes are plotted within the tribe or subfamily to which the fossil belongs. *Victoriapithecus*, on the far left, is widely thought to be ancestral to the split between the Colobinae and the Cercopithecinae (which includes Cercopoithecini and Papionini, shown here) [99]. Notice that for all but two of the PMM ASR-fossil pairs, the ASR estimate is lower than the observed fossil values. Similarly, for all but two of the MMC ASR-fossil pairs, the ASR estimate is also lower than the observed values. These differences are shown quantitatively in Figure 4.

**Figure 4.** Bivariate plot of the difference between ASR trait values and fossil evidence for PMM and MMC. Geological age is shown on the *X*-axis. On the *Y*-axis, we report the absolute value of the difference between the ASR-estimated trait value for each node (molecular divergence) and the trait values observed for the African cercopithecid fossil genera in the same Tribe living near the time of the molecular divergence. The genera are shown in separate colors, defined in the key to the right. Triangles represent the PMM trait, and circles represent the MMC trait. The average difference for PMM is indicated by the top dashed line. The average difference for MMC is indicated by the lower dashed line. *Procercocebus* and *Soromandrillus* are included twice, as they could represent the ancestral morphology for nodes 28, 29, and 30.

#### **5. Discussion**

As advances in genetics and developmental biology make it possible to elucidate the relationship between genotype and phenotype (G:P), paleontologists are able to modify their approaches to anatomical variation accordingly. Our aim in this study was to understand how the method of trait definition influences the ability to reconstruct phylogenetic relationships and evolutionary history inCercopithecidae, the Linnaean Family of monkeys currently living in Africa and Asia. We compared one of the most classic traits in primate paleontology, two-dimensional occlusal tooth size (calculated as the mesiodistal length of the crown multiplied by the buccolingual breadth), to a trait that reflects developmental influences on molar development (the inhibitory cascade, IC [55]) and two traits that reflect the genetic architecture of postcanine tooth size variation defined through quantitative genetic analyses: MMC and PMM [38].

We first established that our maxillary trait types are highly heritable (albeit sensitive to low sample sizes), indicating that variation in tooth size, however it is assessed, is significantly influenced by genetic variation. This result was expected, as it builds on many decades of quantitative genetic analyses of dental variation demonstrating that tooth size is one of the most heritable phenotypes (e.g., [40]). At first glance, there are two caveats to this conclusion. First, while the right IC heritability estimate is significant, the left is not. We know from past analyses that antimeres (left and right side corresponding traits) generally return genetic correlations of one, indicating that they are influenced by identical genetic effects [41,42,50,51,100]. Therefore, we are confident that the left IC is also heritable, similarly to the right, and that our analysis is just underpowered by the small sample size. The second caveat is that we found that both left and ride side maxillary MMC traits returned non-significant heritability estimates. This was not unexpected given the small number of individuals (*n* = 191 for the left and 140 for the right) with data available. We

are confident that this non-significant result is due to the analysis being underpowered rather than a true biological signal, given that the component dimensions when analyzed individually are highly heritable [42,50,51], and that the mandibular homologue of this trait is significantly heritable [38]. However, that said, further analyses with larger sample sizes are clearly needed.

These quantitative genetic analyses provide a good example of how challenging this approach can be, and why this type of research within evolutionary biology is only now becoming more common. Sampling is a significant challenge. For example, in our data set for the SNPRC baboons, composite traits reduce the number of individuals that can be included by a remarkable degree, especially for traits that include measurements of the third molar. We see this data reduction because the SNPRC measurements were collected from dental casts made of living animals. Consequently, the gumline often obscures the back edges of the third molar. Therefore, in a sample of 611 animals within the SNPRC colony, we only have M3 mesiodistal lengths for 140 (right side) and 191 (left side) individuals. Another significant factor in the success of quantitative genetic analyses is the location of the individuals within the pedigree. For example, even though we have more SNPRC baboon individuals available for the analysis of the left IC (*n* = 170) compared to the right (*n* = 127), only the right value returned a significant heritability estimate for IC. This is likely the result of where those individuals with data fall in the pedigree rather than evidence of a different biological signal. We are currently in the process of expanding the SNPRC dental data set and anticipate revisiting these analyses with a larger sample size.

Ever since Darwin [101], biologists have recognized that the heritable nature of phenotypic variation is central to the theory of evolution by natural selection. While all paleontologists appreciate this fact, ascertaining heritability is not simple. Even though the fundamental concept of quantitative genetics originated with Mendel, the ability to analyze the inheritance of normal, continuously varying traits across complex pedigrees was not possible until recently, as the algorithms are computationally intense and require modern computing technologies (for a history of approaches to dental variation: [40]). The modern concepts of evolutionary quantitative genetics were developed almost forty years ago [102–105], but it has been over the last 20 years that there has been an incredible expansion of quantitative genetic analyses being applied to evolutionary questions (examples of this research using primate models: [38,43,46–49,100,106–110]).

In addition to the high heritability estimates, we also find that G:P-mapped traits are phylogenetically conserved and show evidence of selection. ANOVA indicates that all six traits vary significantly across the cercopithecid clade, however, there are interesting differences in how variation in these traits is distributed across the Linnaean families, tribes, and genera. Within the colobines, *Presbytis* is significantly different in terms of twodimensional molar size from other colobines, but not for the G:P-mapped traits. Previous researchers noted that the maxillary M3 morphology and eruption sequence of *Presbytis* sets it apart from other Asian colobines [111,112]. The lack of significant variation in the G:P-mapped traits for *Presbytis* poses the hypothesis that the distinct M3 morphology of *Presbytis* compared to other Asian colobines is not due to variation in the dental genetic architecture of PMM, MMC, or IC. Perhaps the unusual *Presbytis* dental morphology is related to body size, as the two-dimensional areas that are significantly different have pleiotropic effects with body size variation, possibly related to degrees of evolutionary dwarfism in this genus [64,113].

The ANOVA also revealed a distinct separation of three of the papionin genera: *Papio*, *Theropithecus*, and *Mandrillus*. These three genera are derived among the cercopithecids in having elongated muzzles, which is well-known to demonstrate positive allometry [114–117]. Looking more closely, we see that *Papio* and *Theropithecus* differ from the other genera in all six dental traits. However, *Mandrillus* differs in the two-dimensional area traits and the IC and MMC, but not PMM. Given that *Mandrillus* may be in a clade more closely related to *Macaca* than *Papio*/*Theropithecus*/*Lophocebus* [93,118], our results suggest that the phenotypic expression of MMC and IC are convergent in these two clades, and that the expressions of PMM differ despite the similarity in overall muzzle elongation. Previous in-depth analysis of the morphological variation of the faces of *Mandrillus* and *Papio* supports the interpretation that their elongated muzzles are convergent [115]. Our G:P analysis offers the first glimpse into the possible genetic mechanisms that may have been co-opted in this example of parallel evolution.

As described in the Introduction, the MMC and the IC are similar conceptually but distinct in their implementation and aims. The "inhibitory cascade" is a model proposed to explain the pattern of molar size variation observed across murines [55]. The IC model is based on the observation that the timing of initiation of the posterior molars is modulated by the growth of the first molar [55], confirming previous research. Lumsden and Osborn [119] and Lumsden [120] observed that all three molars develop from the ectopic transplantation of just the mouse M1 germ. By measuring the daily growth of mouse molars from 14 to 23 days post-fertilization, Sofaer [121] found compensatory changes in growth rate that seem to result from "some kind of competitive interaction" between the molars [121]. Lucas et al. [122] also observed that for 67 primate species, the size of the maxillary M2 is stable in accounting for 33–40% of the size of the molar row, with the M1 and M3 varying around the M2 in a compensatory manner. Kavanagh et al. [55] provided more experimental evidence for the mechanism first identified by the earlier investigators, gave it a name, and tested the model across the dental variation within Murinae. Since then, the authors have extended it to be a "simple rule govern[ing] the evolution and development of hominin tooth size" [61,123].

When the MMC and PMM were first proposed, we described the variation captured by MMC as likely due to the same developmental mechanisms underlying the IC [38]. However, we named our measurement in terms of the anatomical structures being assessed (the components of the molar genetic module) rather than by a hypothetical developmental mechanism [55], the genetics of which have not yet been established to our knowledge. Therefore, the MMC is not a developmental model likethe IC (contra [124]), but rather a measurement protocol for assessing molar size variation. As Lucas et al. [122] noted, the M1/M3 ratio is a measure of the shape of the tooth row. IC and MMC both capture this shape variation through ratios, but with a distinct difference. The IC is based on the two-dimensional size of M3 divided by the two-dimensional size of M1 (the traditional anatomical assessment of tooth size). In contrast, the MMC is the ratio of the length of M3 divided by the length of M1, which focuses the ratio on the genetic effects that result in variation in the relative lengths of the molars, separatefrom the genetic effects that influence molar width and also body size [38,49]. This distinction between the genetic architecture of length and width dimensions accords with Sofaer et al. [125]'s conclusion that mesiodistal lengths and buccolingual widths are influenced by different genetic and environmental effects, as well as Marshall and Corrucini [126]'s observation that molar lengths change much more slowly than widths in marsupial lineages with evolutionary dwarfing. Based on all of this evidence, the MMC is in all likelihood a more precise reflection of the genetic patterning mechanism that influences molar size proportions in cercopithecids, if not primates and other mammals more generally, compared to the IC.

Our analyses presented here further support the interpretation that the IC and MMC overlap in the genetic influences on molar size variation that they capture. For example, in the quantitative genetic analyses, the IC, MMC, and PMM all have much smaller covariate effects compared to two-dimensional areas (0.05 on average compared to 0.38 on average, respectively). Additionally, the IC and MMC have the same pattern of significance across genera in our ANOVA. This molar module pattern is distinct from the PMM, providing additional evidence that PMM is capturing a genetic mechanism distinct from that of the MMC (and IC). Our estimation of Blomberg's K also reveals similarities between MMC and IC. However, the results presented here suggest that our measurement protocol for MMC may well be a more specific reflection of the underlying genetic mechanism influencing molar proportions in cercopithecids compared to IC, given that we removed the known

pleiotropic effects with body size. Further genetic analyses are needed to explore this with more certainty.

There has been a lot of enthusiasm for what G:P-mapped dental traits might offer for oral health [127] as well as paleontology (e.g., [128,129]). Evans (of [123]) even suggested that for hominids "This pattern is so strong, we can predict the size of the remaining four teeth without even finding the fossils!" (http://evomorph.org/inhibitory-cascade, accessed on 17 July 2022). With evolutionary biologists expressing this type of sentiment about the utility of fossils, it would not be unreasonable for funding agencies and budding scientists to ask if field paleontology is a thing of the past. Does the future of paleontology need new fossils?

In light of this question, our second major aim was to investigate G:P-mapped traits within the fossil record. For this, we focused on the maxillary MMC and PMM and compared computer-generated estimates of ancestral traits to the traits observed on fossils. We want to be up front about there being no clear consensus on direct ancestor-descendant relationships among cercopithecids over the last five million years, as the African cercopithecids from the Plio-Pleistocene are remarkably different from extant monkeys [95,99]. Consequently, new approaches are clearly needed, and G:P-mapped traits might offer novel insight into this murky evolutionary history.

Our comparisons of the ASR estimates with the fossil values unequivocally demonstrate that ASR based on extant data is compromised by the phenomenon of "the tyranny of the present". The lure of the extant comparative data available in museum collections unintentionally limits our expectations for what ancestral morphologies could have been. For example, we find that both MMC and PMM ASR estimates return values lower than what is observed in the fossil record penecontemporaneous with the ancestral nodes (Figure 3). PMM is underestimated twice as much as is MMC (Figure 4). Anecdotally, Figure 3 shows that ASR essentially averages the observed variation and is therefore unable to predict a wider range of variation than that of the input. While paleontologists are sometimes able to input fossil morphologies into their analyses to avoid this bias (e.g., [130]), this requires a high degree of confidence in the ancestor-descendant relationships, something we do not have for the Cercopithecidae. For monkeys, the modern bias in ASR would lead to the interpretation of the PMM of *Papio* and *Theropithecus* as newly derived, when we see that they actually have quite similar PMM values to early papionin genera such as *Parapapio*, *Pliopapio* and *Soromandrillus*. The high MMC values of the Miocene and Pliocene colobines also change how we view the evolutionary relationship of the African and Asian colobines. Knowing that earlier colobines in Africa had higher MMC values than both extant African and Asian colobines suggests that the African and Asian colobines evolved along the same MMC trajectory (reducing the MMC over time). None of these trends are visible when just size alone is considered.

The next step is to figure out what genetic mechanisms MMC (and IC) and PMM capture. We have a few hints. Previous analyses have shown that mandibular MMC is likely more evolutionarily conserved than PMM within catarrhine primates [38], across Boreoeutheria [53], between the different genera of megabats [54], and in the fossil record of the hominids [52]. Our results here for cercopithecids similarly demonstrate that the genetic mechanism captured by maxillary PMM appears to be more evolutionarily labile than maxillary MMC. We report elsewhere that variation in MMC may covary with prenatal growth rates [131], and therefore, MMC, a dental trait, may actually reflect life history variation rather than mastication and diet. If future analyses bolster this conclusion, G:Pmapping of dental variation opens a new window to the paleobiologies preserved in fossil morphology. But without the fossil evidence, we will never fully understand the range of variation that has existed over the evolutionary history of the Cercopithecidae. Therefore, the discovery of new fossils is not only still relevant, but even more revelatory as we apply 21st century methods to this most ancient data set.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/biology11081218/s1, Table S1: Descriptive Univariate Statistics.

**Author Contributions:** Concept development. Performed ANOVA, phylogenetic, and ASR analyses. Drafted an earlier version of the study. Edited the manuscript, T.A.M.; Concept development. Compiled fossil data. Created all figures. Edited the manuscript, M.F.B.; Concept development. Performed quantitative genetic analyses. Edited the manuscript, M.C.M.; Concept development. Ran phylogenetic analyses and consulted with T.A.M. Edited the manuscript, C.A.S.; Ran all univariate descriptive statistics. Created Table 4. Edited the manuscript, C.E.T.; Concept development. PI for the NSF grants that funded much of this research. Organized and wrote the manuscript, L.J.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** Data collection was conducted over the last 24 years and supported variously by funding from the University of California Berkeley (through the Human Evolution Research Center, Department of Integrative Biology, Museum of Vertebrate Zoology, and Museum of Paleontology), Palaeontological Scientific Trust (PAST), Swiss National Science Foundation, The Leakey Foundation, University of Illinois Urbana-Champaign, Western Washington University, and U.S. National Science Foundation grants (BCS 0500179, 0616308, and 0130277) to L.J.H. and NSF DGE 1752814 to C.E.T. M.F.B. is supported by the John Templeton Foundation.

**Institutional Review Board Statement:** All procedures involving animals were reviewed and approved by Texas Biomed's Institutional Animal Care and Use Committee. SNPRC facilities and animal use programs at Texas Biomed are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International, comply with all National Institutes of Health and U.S. Department of Agriculture guidelines, and are directed by Doctors of Veterinary Medicine.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available through the publications cited herein and the published dataset [65].

**Acknowledgments:** We are grateful to Mary Schweitzer and Ferhat Kaya for the invitation to contribute to this special issue, giving us the opportunity to place our research in this broader paleontological context. We thank the repositories and their personnel who provided access to extant and fossil specimens included in this study: American Museum of Natural History, Anthropology Department at the University of Zurich, Cleveland Museum of Natural History, National Museum of Ethiopia (Addis Ababa), University of California Museums of Paleontology (UCMP) and Vertebrate Zoology (MVZ), and the Smithsonian Institution's National Museum of Natural History. We thank the following people for assistance with data collection and/or project development: Julia Addiss, Stephen Akerson, Sarah Amugongo, Liz Bates, Josh Carlson, Selene Clay, Josh Cohen, Theresa Grieco, Anne Holden, Michaela Huffman, Daniel Lopez, Kurtis Morrish, Jackie Moustakas, Alicia Murua-Gonzalez, Danelle Pillie, Whitney Reiner, Oliver Rizk, Antoine Souron, Risa Takenaka, Kara Timmins, Mallory Watkins, Andrew Weitz, Madsen H. White, Tim White, Jeffrey Yoshihara, Sunwoo Yu, and Arta Zowghi. We thank the Texas Biomedical Research Institute (Texas Biomed) in San Antonio, Texas, supported by NIH National Center for Research Resources Grant P51 RR013986, for access and assistance with the quantitative genetic analysis of baboon dental variation. We also thank Eric Delson and colleagues for access to data downloaded from PRIMO, the NYCEP PRImate Morphology Online database (http://primo.nycep.org accessed on 1 November 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Morphological and Tissue Characterization with 3D Reconstruction of a 350-Year-Old Austrian** *Ardea purpurea* **Glacier Mummy**

**Seraphin H. Unterberger 1, Cordula Berger 2, Michael Schirmer 3, Anton Kasper Pallua 4, Bettina Zelger 5, Georg Schäfer 5, Christian Kremser 6, Gerald Degenhart 6, Harald Spiegl 7, Simon Erler 7, David Putzer 8, Rohit Arora 8, Walther Parson 2,9 and Johannes Dominikus Pallua 2,5,8,\***

**Simple Summary:** As glaciers disappear, animal mummies preserved in ice for centuries are released. Depending on the preservation method, residual soft tissues may differ in their biological information content. Paleoradiology, including micro-computed tomography (micro-CT) and magnetic resonance imaging (MRI), is the method of choice for the non-destructive analysis of mummies. A 350-year-old Austrian *Ardea purpurea* glacier mummy from the Öztal Alps was identified with micro-CT, MRI, histo-anatomical analyses, and DNA sequencing.

**Abstract:** Glaciers are dwindling archives, releasing animal mummies preserved in the ice for centuries due to climate changes. As preservation varies, residual soft tissues may differently expand the biological information content of such mummies. DNA studies have proven the possibility of extracting and analyzing DNA preserved in skeletal residuals and sediments for hundreds or thousands of years. Paleoradiology is the method of choice as a non-destructive tool for analyzing mummies, including micro-computed tomography (micro-CT) and magnetic resonance imaging (MRI). Together with radiocarbon dating, histo-anatomical analyses, and DNA sequencing, these techniques were employed to identify a 350-year-old Austrian *Ardea purpurea* glacier mummy from the Ötztal Alps. Combining these techniques proved to be a robust methodological concept for collecting inaccessible information regarding the structural organization of the mummy. The variety of methodological approaches resulted in a distinct picture of the morphological patterns of the glacier animal mummy. The BLAST search in GenBank resulted in a 100% and 98.7% match in the cytb gene sequence with two entries of the species Purple heron (*Ardea purpurea*; Accession number KJ941160.1 and KJ190948.1) and a 98% match with the same species for the 16 s sequence (KJ190948.1), which was confirmed by the anatomic characteristics deduced from micro-CT and MRI.

**Keywords:** magnetic resonance imaging; micro-computed tomography; radiocarbon dating; DNA barcoding

**Citation:** Unterberger, S.H.; Berger, C.; Schirmer, M.; Pallua, A.K.; Zelger, B.; Schäfer, G.; Kremser, C.; Degenhart, G.; Spiegl, H.; Erler, S.; et al. Morphological and Tissue Characterization with 3D Reconstruction of a 350-Year-Old Austrian *Ardea purpurea* Glacier Mummy. *Biology* **2023**, *12*, 114. https://doi.org/10.3390/ biology12010114

Academic Editors: Mary H. Schweitzer and Ferhat Kaya

Received: 22 September 2022 Revised: 6 January 2023 Accepted: 9 January 2023 Published: 11 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Glaciers release animal mummies preserved in the ice for centuries or millennia due to climate changes. These mummies are of inestimable value from an archaeological and biological point of view. Glacier animal mummies are relatively recent, as global temperature evolution has shown pronounced warming over the past 150 years. The location of such glacier animal mummies can be natural habitats, hiding places from predators, migration paths, or transport areas through updrafts [1]. Such animal mummies provide the unique opportunity to evaluate and compare different procedures for the analysis of glacier animal mummies, which can then be applied to human glacier mummies. The histological analysis of soft tissues may further expand the information content [2]. Internal organs, such as those comprising the digestive system, are often entirely decomposed. Organs may be shrunken and challenging to identify. The most oft-preserved soft tissues are those with a high collagen content, such as the dermis, muscle fasciae, and tendons [3].

The presence of skin may give essential clues regarding pathology and trauma. Good soft tissue preservation may also indicate good DNA preservation, allowing for genomic species identification [4]. Ancient DNA studies have proven the possibility of extracting and analyzing DNA preserved in skeletal remains and sediments for hundreds of thousands of years [1–4]. Such discoveries assist scientists in answering questions about extinct species and their relationships to others, including animals alive today. However, the very presence of soft tissue, especially the skin, also makes it challenging to examine the body in a non-destructive manner. Many studies focus on developing and applying nondestructive methods for analyzing mummies, for which paleoradiology is the method of choice [5]. Paleoradiologic analyses enable mummies to remain intact, protecting a valuable archaeological resource. These analyses could disclose information on the nature of the skeletal remains and the mummification process.

Generally speaking, computed tomography (CT) is the gold standard diagnostic method for mummy studies [6]. In addition, magnetic resonance imaging (MRI) has successfully been applied to ancient specimens [7]. The soft tissues that are found in mummified remains display radio-anatomical characteristics that are different from those known from clinical data. In ancient mummies, post-mortem dehydration and decomposition often lead to skin folding, and soft tissues appear radio-opaque on CT [8]. The usual lack of moisture in historical material makes MRI exceedingly tricky. MRI has successfully been applied in ancient dry soft tissues after invasive, morphology-alternating rehydration [9,10] and by using MRI settings with ultra-short echo time sequences [7,11]. However, paleoradiologic diagnostic accuracy and spatial tissue differentiation in historic mummies are not satisfactory due to tissue alterations. Therefore, the desire to achieve a high degree of diagnostic sensitivity and specificity is crucial in choosing any methodological approach in studies on glacier animal mummies. The discovery of one of the best-preserved human glacier mummies in the Ötztal Alps laid the foundations for scientific endeavors to diagnose glacier-bearing objects [12–16]. Due to the rarity of these findings, there is no standardized process for investigation. The handling and examination of glacier mummies are complex due to their rare occurrence and the associated lack of experience.

Micro-CT is an imaging procedure that is based on the same physical and technical bases as CT. These devices are primarily a miniaturized form of volume- or cone-beam CT scanners and can be used for non-invasive, three-dimensional investigations in preclinical research on bones, teeth, and small animals. A significant advantage of using micro-CT compared to clinical CT is a considerably higher spatial resolution with significantly better visualization of anatomical structures [17–19]. In vivo measurements with a spatial resolution of 10 μm are possible [20,21]. Thus, micro- and nano-CTs comprise an essential non-destructive tool to study internal structures in various disciplines, including biology [22–27], paleontology [28], geology [29], thermochronology [30], hydrology [31], soil science [32–35], materials science [36,37], and medicine [38–43].

In order to conduct a comprehensive non-destructive investigation, MRI was added. MRI provides a non-invasive tool to investigate the internal anatomy and physiology

of living organisms and exploits the phenomenon of nuclear magnetic resonance. With this method, atomic nuclei exposed to a strong magnetic field absorb and reemit electromagnetic waves at characteristic frequencies, providing information on the structural and biochemical properties of the tissue [44,45]. Therefore, micro-CT and MRI analyses might offer a valuable and novel extension to conventional methods for glacier mummy research. Ideal tools for the morphological and histomorphological evaluation of mummy specimens include fixation, embedding, cutting, mounting on slides, staining, and examination using microscopic techniques such as optical, electron, and fluorescence microscopy. These methods require substantial preparation procedures and interpretation by experts. Micro-CT and MRI represent complementary tools, enabling the investigation with or without sample processing before image acquisition [46]. These imaging techniques are powerful tools, with several advantages in the structural characterization of biological systems: nearly no damage to samples occurs, and repeated scanning of the same sample is possible [47]. Moreover, imagery from micro-CT and MRI measurements can also be viewed and reviewed in 2D or 3D, and objects of interest can be segmented from the images as digital surfaces or isosurfaces to analyze complex structures [30].

A glacier mummy was found at the Gurgler Ferner on North Tyrolean territory near Ötztal, Tyrol, Austria. This study's primary goal was to apply paleoradiological imaging in the form of micro-CT and MRI analyses, followed by radiocarbon dating and DNA analyses, to this animal glacier mummy. Thus, modern methods for investigating glacier mummies are explored for potential use in human glacier mummies.

#### **2. Materials and Methods**

#### *2.1. Find Spot*

A glacier mummy was found at 3004 m height on 3 August 2015 by Franz Scheiber and Josef Klotz in the area of the Hochwildehaus towards Hochwilde and the Annakegele at the Gurgler Ferner (degree of latitude: 46.785946, degree of longitude: 11.003878) on North Tyrolean territory near Ötztal, Tyrol, Austria (see Figure 1A). The Gurgler Ferner in Tyrol is one of the largest glaciers in the Ötztal Alps. With an area of 9.58 km2, it is now the third-largest glacier in the Austrian province of Tyrol [48]. Due to the demarcation of the border, which in this area is not always oriented to the ice or watershed, smaller parts of the glacier are also located on Italian territory and are protected in the South Tyrolean nature park Texelgruppe. The Gurgler Ferner is a typical valley glacier and flows from the Gurgler ridge, which is part of the main alpine ridge, almost eight kilometers to the north into the Gurgler valley [49]. The Gurgler Ferner is embedded between the Ramolkamm with the Schalfkogel in the west and the Schwärzenkamm in the east. On the orographic right bank of the glacier lies the Hochwildehaus. In this area, on 6 August 2015, Franz Scheiber, Josef Klotz, Judith Unterberger, and Seraphin Unterberger collected the mummy parts (see Figure 1B). All parts were sealed in bags and boxes. The head was separated from the body, and the plumage was packed separately in a plastic bag with undefinable parts when recovered (see Figure 1C,D). These boxes and bags were frozen at −18 ◦C until the start of the investigation.

**Figure 1.** (**A**) Location of sample collection of the glacier mummy on the Gurgler Ferner using tirisMaps (https://maps.tirol.gv.at, accessed on 22 November 2022). This location is west of the sample collection of Ötzi, about 11.16 km apart. (**B**) Presentation of the glacier mummy at first finding with (**C**) focus on the corpus and the (**D**) detailed skull.

#### *2.2. Micro-Computed Tomography (Micro-CT)*

Micro-CT measurements were performed on a vivaCt40 and an XtremeCT II (ScancoMedical AG, Brüttisellen, Switzerland). Due to the geometrical dimensions, the corpus of the mummy was scanned only in the XtremeCT II. The mummy's skull was further analyzed in the vivaCT40 due to its higher resolution. The settings for the XtremeCT II experiments were a 30.5 μm isotropic voxel size with a 68 kV, 1470 μA tube setting and 650 ms exposure time. The image matrix was 4096 × 4096, with a 16-bit grey-value resolution. The settings for the micro-CT experiment were a 10.5 μm isotropic voxel size with a 70 kV, 114 μA tube setting, 6500 ms exposure time, 1000 projections, and 2048 samples. The image matrix was 2048 × 2048 with a 16-bit grey-value resolution. The micro-CT data were evaluated by an experienced radiologist and summarized. The reconstructions were carried out with Analyze 14.0 (Analyze Direct Inc., Overland Park, KS, USA) software. The following two-dimensional display formats were used for the reconstructions:

Various three-dimensional reconstructions were also used:

For the comparative morphological study, 12 individual bones (sternum, coracoid, scapula, furcula, humerus, radius, ulna, carpometacarpus, pelvis, femur, tibiotarsus, and tarsometatarsus) of the postcranial skeleton were used and compared with published data [50]. The measurements were carried out as described in [50].

#### *2.3. Magnetic Resonance Imaging (MRI) Data Acquisition and Processing*

The MRI experiments were performed on a 3-T MR-Scanner (Siemens Magnetom Skyra, Siemens Healthineers, Erlangen, Germany) using a standard 12-channel head coil. Magnetization prepared T1 weighted ultrashort echo time imaging using PETRA ("Pointwise Encoding Time Reduction with Radial Acquisition") was used [51] with TR = 3.32 ms, TE = 0.07 ms, TI = 1300 ms, flip angle: 6◦, receive bandwidth: 400 Hz/pixel, number of radial views: 60,000, FOV: 251 mm, image matrix: 320 × 320, voxel size: 0.78 mm × 0.78 mm × 0.78 mm. In addition, a multi-slab T2-weighted turbo spin-echo sequence was acquired with TR = 6680 ms, TE = 103 ms, echo train length: 15, slice thickness: 2 mm, spacing between slices: 2.2 mm, acquisition matrix: 448 × 314, FOV: 140 mm, voxel size: 0.31 mm × 0.31 mm × 2 mm, number of slabs to cover the whole bird: 4 with 25 images per slab. Data processing and analyses were performed using Syngo.Via (Siemens Healthcare, Erlangen, Germany).

#### *2.4. Sample Collection and Tissue Specimens*

First, the animal glacier mummy was macroscopically examined. After using the paleoradiological methods, two independent biopsies were taken from different organs. As these did not provide conclusive results, the mummy was then sequentially sliced from caudal to cranial at 3–5 mm intervals, fixed in formalin, and embedded in paraffin as wholemount sections according to the European standards of Biobanking CEN/TS and the ISO standards ISO 20166-1:2018, ISO 20166-2:2018, and ISO 20166-3:2018 on the pre-examination process for molecular diagnostics [52,53]. This technique assures the preservation of tissues for future histological and biomolecular analyses. Before formalin fixation, samples were taken for radiocarbon dating and DNA sequencing. Figure 2 presents the macroscopical inspection and sampling for DNA sequencing and radiocarbon dating.

#### *2.5. Radiocarbon Dating*

Radiocarbon dating was routinely performed at the Ion Beam Physics, ETH Zurich Laboratory. Two samples (20 mg and 200 mg) consisting of feather, skin, bone, and tissue were used. Sample treatment [54,55], reporting [56,57], and reporting 14C ages [58] were conducted according to the cited literature.

**Figure 2.** Sample collection. (**A**) Sample for DNA sequencing; (**B**) 3–5 mm slice of the mummy; (**C**) overview of the sequentially sliced mummy from caudal to cranial at 3–5 mm intervals; (**D**) paraffin-embedded tissue blocks.

#### *2.6. DNA Analysis*

Two tissue punches (size 1.5 × 2 mm) and one bone sample from the glacier mummy were used for the DNA analysis. The tissue punches were lysed, and DNA was extracted using the EZ-1 and the MagAttract DNA kit (all Qiagen, Hilden, Germany) following the manufacturer's recommendations.

Physical and chemical cleaning of the bone surface: The mechanical and chemical processing of the samples was performed with the necessary care required for forensically relevant samples containing only minute amounts of DNA exposed to potential superficial contamination [59,60]. One bone sample was taken from the unknown animal glacier mummy and subjected to mechanical surface cleaning with sterile scalpel blades. The sample was then bathed in sodium hypochlorite (≥4% active chlorine, Sigma Aldrich, St. Louis, MO, USA) at room temperature for 15 min, washed twice in purified water (DNA/RNA free), and rinsed in absolute ethanol for 5 min. Samples were dried in a closed laminar flow cabinet overnight, UV irradiated for 10 min (λ = 254 nm), and then powdered using a vibrating ball mill (Mixer Mill MM400, Retsch, Haan, Germany). Grinding with the ball mill was performed in cycles of 60 s, with a grinding rate of 25 Hz, followed by 60 s cooling steps. A minimum of two grinding cycles were completed, and the abrasive product was visually evaluated for homogeneity.

DNA extraction of the bone sample: The bone powder was subjected to lysis, and DNA was extracted according to the modified Dabney method as described in Xavier et al., 2021 [61].

Mitochondrial DNA typing: Both the mitochondrial (mt)DNA cytochrome b gene [62], as well as the 16 sRNA [63], were amplified and sequenced on an ABI 3500 Sequencer, and the sequences were aligned using Sequencher (GeneCodes, Ann Arbour, MI, USA). The consensus sequence was BLASTed at NCBI GenBank to recover the closest neighbors for species identification.

#### **3. Results**

#### *3.1. Radiocarbon Dating*

Data of the radiocarbon dating of the *Ardea purpurea* glacier mummy are shown in Figure 3. The sample indicates the presence of "bomb peak 14C" (post 1950 AD) 1 sigma range BC/AC Lower 1642 Upper 1665, 2 sigma range BC/AC Lower 1529 Upper 1799. All calibrated intervals listed below need to be taken into account. In some cases, due to the shape of the calibration curve in the region of interest, the sample's age falls into a period when precise information about the true age range cannot be provided. Therefore, radiocarbon dating defined the mummy's age as 350 years.

**Figure 3.** Radiocarbon dating of the *Ardea purpurea* glacier mummy. 14C age (BP)—delta C13 corrected radiocarbon age based on concentration of 14C measured in sample. BP = before present (before 1950 AD) [56,57].

#### *3.2. Morphological Analysis via Micro-CT and MRI*

Before further destructive analyses, micro-CT and MRI were used with photographs of anatomical sections to study anatomy. The micro-CT and MRI scans were correlated with three photograph images of the anatomical section (see Figure 4) to identify relevant structures along the trunk from the crop to the end of the thoracoabdominal cavity. A localization image for micro-CT and MRI is shown in Figure 4, with the transverse planes and corresponding anatomical sections shown in lines. Micro-CT, MRI, and anatomical section examination revealed the presence of remains of internal organs, many of which appeared to be lytic. Brain tissue remains were not visible on the inside of the calotte. The remains of the lungs, heart, stomach, and other internal organs were also visible. The lungs are collapsed, and their outlines were still recognizable. The alveolar and bronchial structures were still clearly visible. The remains of all major muscle groups were also preserved. Fat deposits were still visible. The examination of the remaining structures revealed no pathological modifications, and the specific cause of death could not be conclusively determined. There were no signs of artificial body mummification (e.g., no puncture channels, no opening of the body cavities, no removal of organs, and no introduction of foreign material).

**Figure 4.** Transversal micro-CT, MRI, and anatomical section images of the trunk at different levels. (**A**) Anatomical section of the trunk at the level of the cor and lung. (**B**) Anatomical section of the trunk at the level of the hepar and lung. (**C**) Anatomical section of the trunk at the level of the cloaca and intestine. (**D**) Scout view of the trunk with lines representing the locations of different levels.

Three-dimensional reconstructions of the skull and the postcranial skeleton based on the micro-CT data of the *Ardea purpurea* glacier mummy were performed for comparative morphological studies on single bones. All bones were in their anatomical position and completely preserved (see Figure 5). The skull was heavily pneumatized, and the posterior skullcap was large. Prominent blood–brain conductors and the bone spur were still present. The beak was deformed but not bent (see Figure 5A,B). Based on the skull, no immediate species identification could be performed due to the severe deformations of the skull. The skeleton showed no fractures, which is demonstrated by the maximum intensity projection (Figure 5C), volume rendering bone of the body (Figure 5D), and volume rendering soft tissue of the body (Figure 5E).

As a result, most of the investigated bone elements fell within the size range of modern *Ardea purpurea*. The dimensions of the coracoid, scapula, humerus, radius, ulna, carpometacarpus, pelvis, femur, tibiotarsus, and tarsometatarsus showed the smallest measured value compared to modern *Ardea purpurea*.

The furcula, coracoid, and pelvis are described as follows in more detail.

Furcula: The characteristic of the furcula of herons is a thorn-like process on the hypocleidium, which extends dorsally at the bifurcation point of the two furcula branches. The hypocleidium bears a distinct suture bar on the caudodorsal side. The best way to distinguish species is the hypocleidium. It is more general in the *Ardea purpurea* and ends, tapering evenly, with two small bone serrations close to each other (see Figure 6).

**Figure 5.** Three-dimensional reconstruction of the skull and the trunk of the *Ardea purpurea* glacier mummy. (**A**) Photograph of the skull. (**B**) Volume rendering bone of the skull. (**C**) Maximum intensity projection of the body. (**D**) Volume rendering bone of the body. (**E**) Volume rendering soft tissue of the body.

**Figure 6.** Three-dimensional reconstruction of the furcula of the *Ardea purpurea* glacier mummy.

Coracoid: Compared to other herons, *Ardea purpurea* has a long and slender coracoid with a high process scapularis and a deep concave margo lateralis. The acrocoracoid, when viewed cranially, is narrow as in *Nycticorax nycticorax*. All herons have a well-developed processus scapularis that curls medially with a broad base. In the *Ardea purpurea*, this tip is drawn more craniodorsally than in other herons. The processus lateralis is bent up in the shape of a hook; together with the edge drawing from it to the apex lateralis, the margo lateralis is an essential distinguishing feature. The edge of the small hook, viewed cranially or caudally, drops vertically to bend into a concave edge. Here, the arcuate notch extends deeper into the coracoid plate. At about two-thirds of the total length, the coracoid shaft widens towards the lateral process. The labium articulare sternale extends evenly in a slight arc from the medial margin to the apex lateralis. The asymmetrical design of the articular grooves at the anterior margin of the sternum causes a different form of the ventral end of the coracoids on both sides.

For this reason, the coracoids of both sides were measured. The ventral section of the right coracoid is slightly wider than that of the left, expressed in the measurements broad basal (BB) and broad of the facies articularis basalis (BF). Additionally, the greatest diagonal length (acrocoracoid–apex lateralis) and medial length (acrocoracoid–apex medialis) were determined (see Figure 7).

**Figure 7.** Three-dimensional reconstruction of the coracoid of the *Ardea purpurea* glacier mummy.

BB; broad basal = 20.67 mm.

BF; broad of the facies articularis basalis = 14.23 mm. GL; greatest diagonal length (acrocoracoid–apex lateralis) = 54.10 mm. LM; length medial (acrocoracoid–apex medialis) = 51.90 mm.

Pelvis: In herons, the praeacetabular portion of the pelvis is about as long as the postacetabular portion. The pelvis of *Ardea purpurea* is small and narrow. The muscle lines further caudally than in *Ardea cinerea* and ends on a small scale of bone. The cranial tip of the crista spinalis protrudes cranially beyond the pars glutaea ossis ilium. The foramina intertransversaria medialia are less numerous and smaller. The 3D reconstruction of the pelvis in Figure 8 clearly shows taphonomic bone loss.

The sternum could not be reconstructed three-dimensionally and therefore could not be measured correctly. The comparative description of the bones and the differentiation criteria published by Kellner [50] could be confirmed using the 3D reconstructions of the bones. Based on the anatomic characteristics published by Kellner [50], the glacier mummy was determined to be an adult male *Ardea purpurea*.

**Figure 8.** Three-dimensional reconstruction of the pelvis of the *Ardea purpurea* glacier mummy. KB: smallest width of the partes glutaeae = 12.45 mm; DA: diameter of the acetabulum (greatest distance) = 7.10 mm.

#### *3.3. DNA Analysis*

The bone sample was successfully examined in the cytb gene region (358 bp (13,733–14,090) and 16 s region (604 bp, 1943–2545—primer sequences included, positions according to reference sequence KJ190948.1). The BLAST search in GenBank resulted in a 100% and 98.7% match of the cytb gene sequence with two entries of the species Purple heron (*Ardea purpurea*; Accession number KJ941160.1 and KJ190948.1) and a 98% match with the same species for the 16 s sequence (KJ190948.1). The two tissue samples did not result in usable sequences.

#### **4. Discussion**

This workup of a serendipitously found glacier mummy from the Gurgler Ferner on the North Tyrolean territory near Ötztal (Tyrol, Austria) allowed for the identification of a male *Ardea purpurea*. This identification was confirmed by DNA analyses and anatomical observations with high matches of the cytb gene sequence and the 16 s sequence using a BLAST search in GenBank. Radiocarbon dating defined the mummy's age as 350 years. Then, paleoradiological techniques, including micro-CT and MRI, allowed for a 3D reconstruction showing the mummy's skull and body for further comparison with previously described anatomical structures [50].

Since mummies are potentially precious relics of past times, non-invasive techniques are preferred, and CT imaging is currently the most common method. The multiple planes with optimized settings for different tissues in micro-CT and MRI images have allowed species identification based on osteoanatomical observations for about 40 years [64]. Micro-CT and MRI images allowed for the three-dimensional reconstruction, especially of the skull and pelvis for sex-specific features and of the sternum, coracoid, scapula, furcula, humerus, radius, ulna, carpometacarpus, pelvis, femur, tibiotarsus, and tarsometatarsus

for bone identification, as previously shown for *Ardea purpurea* [50]. Only the sternum and parts of the pelvis were severely degraded in the glacier mummy (e.g., leaching of calcium from the bones) and could not be fully morphologically assessed. As a result of the microenvironment, demineralization may not be uniform within the skeletal system or bone. The bone may appear patchy despite being morphologically intact.

Moreover, other tissues seem to become more radio-dense (i.e., the attenuation of X-ray beams increases). In particular, this affects ligaments, fasciae, and the subcutis [3]. This may be due to the deposition of mineral salts (containing metals such as iron) in collagenous tissues. Organs may require manual segmentation in several slices due to the changes caused by water loss. Therefore, the images need to be post-processed by segmenting, delineating, and extracting specific anatomical structures.

Complementing CT images with MRI data is desirable due to the lack of contrast in soft tissues found in CT images. Mummified tissues are invisible to standard MRI techniques due to their dehydration, short T2 relaxation times, and special acquisition, and thus strategies such as ultrashort echo time sequences (UTE) have to be used [65]. The magnetic resonance effect is observed for any atomic nucleus with special magnetic properties (magnetic moment), but hydrogen nuclei (protons) in particular have very advantageous properties. Therefore, protons (i.e., hydrogen) are usually most relevant for MRI, and images show the tissue's water content or the properties of water within the tissue. Water content is higher in living tissues than in bone and enamel, which are highly mineralized, with little water content. In addition to the short T2 relaxation times already mentioned, this also explains the reduced value of MRI in mummies. However, in the glacier mummy studied in this work, the water content was high enough to allow for MRI (Figure 5).

As a limitation of the imaging methods, there is a certain degree of subjectivity in the segmentation process. Detailed anatomical knowledge is necessary. Overall, CT and MR scanning images and 3D renderings of internal structures and tissues should not be viewed as objective and "true" representations. Visualizing the skeleton results in many points of reference, allowing for a more accessible assessment of the remains of internal organs and structures. Pathological processes can be falsely attributed to diagenetic processes and vice versa [64]. In mummy MRIs, the greatest challenge is the extensive dehydration of the tissues, since dehydrated tissues lack the hydrogen (H) in mobile water required for standard MRI signals [65]. MRI imaging is less informative than micro-CT imaging in mummies without sufficient water content. This can be circumvented by rehydrating tissues and organs [10] (but this is an invasive procedure and may not always be possible) or by applying the technique to mummies that are not entirely dehydrated, such as this glacier mummy [66]. Accordingly, reasonable images using MRI have been reported for Ötzi the Tyrolean Iceman, Lindow Man from a bog, and a corpse from a sealed medieval Korean tomb [65].

Several confounding factors, including taphonomic processes and preservation always limit the study of mummies. We estimate that over a hundred mummies have been CT-scanned and reported at this point [64]. None of these data have been synthesized into meaningful work beyond a single individual scan. Often, insufficient imaging data on ancient tissues prevents conclusions from being drawn. Future research is needed to determine the difference between antemortem and postmortem findings in micro-CT and MRI images in long-term observations to eventually produce the most accurate results with optimized segmentation procedures. Comparisons between image-based and specimen-based information are necessary. Mummified tissue biobanks with asservation of tissue specimens have already been proposed [3]. Thus, new trends in mummy research emphasize establishing guidelines and ensuring proper scientific methodology regarding analytic methods.

#### **5. Conclusions**

Applying the methodological concept of micro-CT and MRI imaging in combination with invasive but established techniques such as radiocarbon dating and DNA analyses can support the identification of animal species, as in the case of this glacier mummy. Three-dimensional digitization and interactive visualization of micro-CT and MRI allowed us to conduct digital autopsies and to provide a highly detailed 3D reconstruction of the *Ardea purpura* mummy.

**Author Contributions:** Conceptualization, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; methodology, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; software, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; validation, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; formal analysis, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; investigation, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; resources, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; data curation, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; writing—original draft preparation, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; writing—review and editing, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; visualization, S.H.U., C.B., M.S., A.K.P., B.Z., G.S., C.K., G.D., H.S., S.E., D.P., R.A., W.P. and J.D.P.; supervision, J.D.P.; project administration, J.D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Verein zur Förderung der Hämatologie, Onkologie und Immunologie, Innsbruck, Austria.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors thank Franz Scheiber and Josef Klotz for finding and recovering the mummy. The authors thank Richard Scheithauer, head of the Institute of Forensic Medicine (Medical University of Innsbruck), for the ongoing support. The authors would like to thank Christiane Böhm, Armin Landmann (both Innsbruck), Peter de Knijff (Leiden), Gerhard Forstenpointer (Vienna), and Julia Waldhart for their fruitful discussions. Thanks to Sabine Jöbstl, Inge Jehart, and Sarah Peer for their excellent technical support.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Ethical Statement:** For such studies without risk of pain for the animal, neither the Ethics Committee nor the Animal Welfare Committee of the Medical University of Innsbruck considered themselves responsible for this project. The Advisory Board on Ethical Issues in Scientific Research and representatives of the animal protection committee of the Leopold Franzens University, as well as colleagues from the University of Veterinary Medicine in Vienna, further concluded that there are no known legal or ethical standards for projects on animal mummies in this country.

#### **References**

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### *Article* **Dental Paleobiology in a Juvenile Neanderthal (Combe-Grenal, Southwestern France)**

**María Dolores Garralda 1,\*, Steve Weiner 2, Baruch Arensburg 3, Bruno Maureille 4 and Bernard Vandermeersch 4**

**Simple Summary:** Numerous prehistoric sites in Europe and the Near East provided bones and dental remains of the populations of the past. One of them is the Combe-Grenal Cave (SW France), where fossils of children and adults represent the Neanderthals who lived there more than 60 ky ago, during a harsh period of the last glaciation. In this paper, we analyze a sample of the tartar of a juvenile individual. The numerous bacteria forming the plaque are compared to those of one adult from Israel, Kebara 2, revealing the differences between the most common bacteria in a young and an older individual, probably because of their immunological systems, and the different living conditions of the human groups they represented.

**Abstract:** Combe-Grenal site (Southwest France) was excavated by F. Bordes between 1953 and 1965. He found several human remains in Mousterian levels 60, 39, 35 and especially 25, corresponding to MIS 4 (~75–70/60 ky BP) and with Quina Mousterian lithics. One of the fossils found in level 25 is Combe-Grenal IV, consisting of a fragment of the left corpus of a juvenile mandible. This fragment displays initial juvenile periodontitis, and the two preserved teeth (LLP4 and LLM1) show moderate attrition and dental calculus. The SEM tartar analysis demonstrates the presence of *cocci* and filamentous types of *bacteria*, the former being more prevalent. This result is quite different from those obtained for the two adult Neanderthals Kebara 2 and Subalyuk 1, where more filamentous *bacteria* appear, especially in the Subalyuk 1 sample from Central Europe. These findings agree with the available biomedical data on periodontitis and tartar development in extant individuals, despite the different environmental conditions and diets documented by numerous archeological, taphonomical and geological data available on Neanderthals and present-day populations. New metagenomic analyses are extending this information, and despite the inherent difficulties, they will open important perspectives in studying this ancient human pathology.

**Keywords:** Neanderthal; Combe-Grenal; juvenile; mandible; periodontitis; tooth; tartar; SEM analysis

#### **1. Introduction**

The Combe-Grenal site is located east of the Domme village (Dordogne), on the right side of the valley of a small Dordogne dried river. The site, facing south-west, corresponds to a probably very deep and large rock-shelter, naturally formed within Cogniacian limestone. Only a small part of the rock-shelter remains, preserving only a narrow and very small cave at its northeastern angle. Such geomorphology describes the site name "grotte de Combe-Grenal" since at least 1817. Combe-Grenal rock-shelter was first excavated by D. Peyrony in 1929, who identified three Mousterians layers, but the most important investigations were those led by F. Bordes, between 1953 and 1965 [1–4].

**Citation:** Garralda, M.D.; Weiner, S.; Arensburg, B.; Maureille, B.; Vandermeersch, B. Dental Paleobiology in a Juvenile Neanderthal (Combe-Grenal, Southwestern France). *Biology* **2022**, *11*, 1352. https://doi.org/10.3390/ biology11091352

Academic Editors: Mary H. Schweitzer and Ferhat Kaya

Received: 1 August 2022 Accepted: 9 September 2022 Published: 14 September 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Since 2014, new scientific fieldwork is in progress under the direction of J.-Ph. Faivre (PACEA, Bordeaux).

Considering human fossil remains, several pieces were found by Bordes in Mousterian levels 60, 39, 35 and 25. Geological and faunal studies conducted by Guadeli and Laville [5] attribute level 60 to MIS 6, level 39 to MIS 5a, while levels 35 to 25 are related to MIS 4. Most of the Combe-Grenal fossils (those from layers 35, and especially 25, should be placed chronologically at the beginning of MIS 4 (~70 to 60 ky) and assigned to its coldest period (~70 to 65 ky). Paleoenvironmental and chronostratigraphic data document climatic changes toward colder conditions, first humid and later increasingly drier, as well as a progression of the open Arctic milieu fauna, confirming the cold and harsh environment in which people then lived [5].

The anthropological fossils found at Combe-Grenal were the objects of detailed morpho-anatomical descriptions and analyses [6–9]. The whole sample is presently preserved at the Musée National de Préhistoire at Les Eyzies (Southwestern France).

Most of the remains were found in level 25, where several young adult males and females (MNI ~8) of different ages were identified [6]. Bordes' unpublished data demonstrate the dispersion of the fossils in several excavation grid squares, very close to one another, located at the center of the back part of the rock-shelter [6]. All the human fossils were fragmented and randomly mixed with abundant faunal remains and lithics. There were no traces of deliberate burials, but cut marks were identified on several fragments [6,7,9]. Morphological and anatomical analyses of the Combe-Grenal fossils have led to their assignment to Neanderthals.

The aim of this contribution is the study of a tartar sample obtained from the mandibular fragment Combe-Grenal IV. We will briefly summarize the interest in dental calculus analyses, followed by the main morphological characteristics of the fossil, the oral pathology and the results of the tartar SEM analysis in comparison with the previously published data from other Neanderthals and new methods of analyses.

#### **2. Brief Reminder of Tartar (Dental Calculus) Etiology**

Tartar or dental calculus is a form of hardened dental plaque, caused by the precipitation of minerals from saliva and gingival crevicular fluid in the tooth's plaque. Such a process kills the bacterial cells within the dental plaque, forming a rough and hardened surface ideal for further plaque formation, namely tartar [10].

Two types of dental calculus have been described. Supragingival tartar affects the gums along the gumline, while subgingival tartar forms within the narrow sulcus existing between the teeth and the gingiva [10]. Dental calculus formation is associated with several clinical manifestations, including receding gums and chronically inflamed gingiva.

According to Lang et al. [10], tartar is composed of both inorganic (mineral) and organic (cellular and extracellular matrix) components. The cells within the dental calculus are primarily bacterial, but also include at least one species of *Archaea* (usually called "*cocci*") and several species of yeast. Trace amounts of dietary and environmental micro debris or plant DNA have also been found.

The processes of dental calculus formation are not well understood. Tartar forms in incremental layers, but the timing and triggering of these events are poorly understood and vary widely among individuals, probably related to age, gender, diet, etc. [10]. Supragingival tartar is more abundant on the buccal surfaces of the upper maxillary molars and the lingual surfaces of the mandibular molars, while subgingival tartar forms below the gumline and is typically dark in color due to the presence of black-pigmented bacteria.

Dental calculus has been described in animals (e.g., [11]) and documented in various human groups and individuals from Prehistory to present times. Sometimes, even if exceptional, tartar can be present as a very thick deposit, such as on the T15 individual from the Medieval cemetery from Clarensac (Gard, Southeastern France; [12,13]). Concerning human fossil teeth, unfortunately, in the past, many of them have been excessively cleaned, destroying and removing tartar deposits. However, we do have a few with preserved

tartar deposits. This is the case for several Early Upper Pleistocene Neanderthals, whose analyses offer new data on their biology and expand the knowledge of their hunter–gatherer population behaviors.

#### **3. Materials and Methods**

#### *3.1. Materials*

Combe-Grenal IV is a fragment of the left side mandibular corpus corresponding to the upper part of the left mandibular body (Figure 1), ranging from the distal margin of the left lower second premolar (from here LLP3) alveolus to the mesial septum of the left lower second molar (LLM2; [6]). It preserves two lower teeth, the lower left second premolar (LLP4) and the first molar (LLM1), both with tartar deposits (Figure 1, red arrows) around the crowns and interproximal facets with the LLP3 and the LLM2 (both absent), which indicate that all four teeth had emerged and were functional.

**Figure 1.** Combe-Grenal IV: external (**A**), internal (**B**), occlusal (**C**), distal side of the LLM1and the alveolus of the LLM2 (**D**), X-ray (**E**) external side. Scales = 10 mm.

On this mandibular fragment, Combe-Grenal IV, we can also observe the alveolus of the LLM2 (Figure 1C,D). It has highly visible osseous trabeculae, and the alveolar ridge is altered by gingivitis (Figure 1 blue arrows), indicating that the LLM2 had fully erupted and was functional long before the individual's death, causing the distal interproximal facet on the LLM1. The LLP4 has the mesiodistal axis slightly oblique in comparison to that of the molar. The crown has traces of occlusal attrition (degree 1; [14]) and two interproximal facets, while the mesial one has a deep vertical sulcus [6].

Few age markers can be considered regarding this incomplete fossil, such as the close apex of the two preserved teeth (Figure 1E), their weak attrition (degree 1 from Murphy; [14]) or the small interproximal facet caused on the LLM1 distal side (Figure 1D) by the (absent) LLM2. It is possible to estimate the age at death of this individual by using the charts on dental eruption published for modern children, although they are based on samples with very different biological and environmental conditions. Thus, according to the Ubelaker [15] schemes and the AlQahtani et al. [16] atlas, the age was

15 ± 3 years and 15.5 years, respectively. Consequently, Combe-Grenal IV can be assigned to a juvenile individual.

The alveolar arch (Figure 1 green arrows) displays a slight degree of resorption and is separated from the tooth cement–enamel junction by 2.5/3.02 mm. The loss of osseous mass and the alveolar destruction can also be seen in the radiograph (Figure 1E), showing a pathology that seems to be the result of incipient periodontal disease. This slight exposure of roots, especially if we consider the lack of conjunctive tissue, varies between 1.0 and 1.5 mm in height [17]. Both preserved teeth on Combe-Grenal IV show supragingival tartar deposits (Figure 1 red arrows) forming a wide band around the crowns, separated by 5/7 mm from the alveolar margin. As in degree 2 on the Brothwell [18] scale, and, according to the classification of periodontal diseases [19], they correspond to stage I (early–mild) and nearly stage II (moderate).

Two pulp stones (pulpoliths) of different sizes (that rattle when shaking the fragment) also appear in the pulp chamber of those teeth (Figure 1E, yellow arrows). Such pathology has been documented in other Neanderthals, as in the case with Combe-Grenal X and 29 [6] and Kebara 2 [20,21].

#### *3.2. Methods*

A small sample of supragingival dental calculus was detached from the lingual surface of the Combe-Grenal IV LLM1 and processed for a scanning electron microscope study at the Weizmann Institute of Science at Rehovot (Israel).

We measured the *bacteria* directly from the micrographs, keeping in mind that these are only estimates. This is because we cannot know whether we are viewing the real length or diameter, given that they were partially embedded in the calcified matrix. The average dimensions were photographed in three pictures, with the different magnifications indicated in Tables 1 and 2. We measured the *bacteria* appearing more complete (avoiding the empty cavities). Each crown measurement was repeated on three different days by two of the authors (S.W. and B.A.), and the inter- and intra-observer error between measurements was <4%.

**Table 1.** Measurements in μm (diameter and length) of the Combe-Grenal IV tartar *bacteria*, taken from the SEM pictures indicated.

On the obtained images, the bacteria identification and measurements were performed by Image Tools 3 "UTHSCA" analysis, and the results are expressed as means ± SE (standard error). The analyses included a breakdown and one-way ANOVA tests. The *p*-values indicated the post hoc significance levels for the respective pairs of means, and a *p*-value of <0.05 was considered significant. The calculations were performed using the SPSS statistical package (1990) and the STATISTICA package (StatSoft Inc., Tulsa, OH, USA, 1995), and their results are given in Table 3.

**Table 2.** The measurements in μm (diameter and length) of the Kebara 2 tartar bacteria, taken from the indicated SEM pictures.

**Table 3.** Statistical analysis of the total lengths and diameters of the tartar *bacteria* from Combe-Grenal IV and Kebara 2.

#### **4. Results**

The two preserved teeth on Combe-Grenal IV, left LLP4 and LLM1, show supragingival dental calculus deposits (Figure 1 red arrows) forming a wide band around the crowns, separated 5/7 mm from the alveolar border, as in degree 2 on the Brothwell [18] scale and I/II of the recent classification [19].

On the surface of the Combe-Grenal IV tartar sample, fine crystal dental calculus deposits appear in the macro photographs at different magnifications. They reveal alternate layers running from the first calcified plaque directly covering the enamel surfaces to the outermost and final calcified layer, indicating various stages in the dental calculus formation of this individual. Magnifications of 3500 μm (Figure 2A), 10,000 μm (Figure 2B) and 20,000 μm (Figure 3A) clearly show both empty bacterial cavities and complete bacteria embedded in the calcified matrix. The bacteria present are *cocci* and filamentous types, although it is not possible to recognize the specific fossilized micro-organisms among the ~325 species that could be present in the oral cavity [22].

**Figure 2.** Combe-Grenal IV. (**A**): (picture 306), tartar *bacteria* at 3500 SEM magnification. (**B**): (picture 304): tartar *bacteria* at 10,000 SEM magnification, white scale = 1 μm.

**Figure 3.** Combe Grenal IV. (**A**): (picture 308), tartar *bacteria* at 20,000 SEM magnification. (**B**): Kebara 2 (picture 603), tartar *bacteria* at 5000 SEM magnification.

We used the SEM images to compare the distribution of these microbiotas in two different Neanderthal fossils: the juvenile Combe-Grenal IV and the adult male Kebara 2 (Israel), for which we have the analysis of a sample also from the calculus of his LLM1 [20].

As can be observed in Figure 3B, the Levantine fossil contains numerous *cocci* bacterial types [20], and rods are more frequent than in the young Combe-Grenal individual.

There are also some differences in the size of the identified bacteria between both individuals. In Tables 1 and 2, the parameters corresponding to the length and diameter (in μm) of the *bacteria* in the compared fossils cited above are given. They were measured on the SEM microphotographs corresponding to the indicated numbers (Combe-Grenal IV: 304, 306, and 308; Kebara 2: 601, 603, and 606).

In Figure 4, the "Box and Whisker Plot" shows the differences obtained for the total dimensions of Combe-Grenal IV and Kebara 2 *bacteria*, with the former (Figure 4, left) corresponding to the diameter and the latter (Figure 4, right) to the length. On both graphs appear the mean values and the variation range of ± 1 and 1.96 standard errors. The differences between the total values obtained for both fossil individuals (Table 3) are statistically significant, particularly those comparing the diameters (*p* < 0.001), indicating larger dimensions of the bacterial flora on the adult male Kebara 2 than on the juvenile Combe-Grenal IV.

**Figure 4.** Comparison (Box and Whisker Plot) of the total diameter (**left**) and length (**right**) measurements (in μm) of the Combe-Grenal IV and Kebara 2 bacteria. The mean and the ranges of variation of ±1 and 1.96 error standard deviations are indicated.

These results agree with the available information on the older adult Subalyuk 1 (Hungary), which—following Pap et al. [23]—shows the presence of more filamentous type *bacteria* than on Kebara 2 [20]. As their dimensions were not given, no statistical comparison with the other two Neanderthals can be made.

#### **5. Discussion**

Tartar, being a mineralized form of dental plaque adhering to the surface of the tooth, can be preserved, and the study of this durable material using SEM provides information on the microbial flora responsible for the periodontal disease of ancient hominid fossils. It is well known that the presence of periodontitis, in general, is the result of a dense accumulation of micro-organisms on the tooth surface and the host response (innate and acquired immunity) of each individual [24].

The most ancient case among hominids is that described by Ripamonti [25] on a juvenile *Australopithecus*. Concerning the Neanderthals, the publications on the *bacteria* found in the dental calculus of Kebara 2 [20,21], Subalyuk 1 [23] and the present paper demonstrate the rich oral flora present in ancient human populations.

The results of the tartar macroanalyses of these three Neanderthals, Combe-Grenal IV, Kebara 2 and Subalyuk 1, also indicate differences in the oral flora causing supragingival dental calculus and periodontitis among them. When interpreting these findings, it is important to consider not only the chronology and environment in which they lived, but also the individual's age at death (more *cocci* on the juvenile) and the available data on diets. The latter is difficult to obtain when dealing with sites excavated long ago. Another influencing factor could be immunological differences between individuals, because each one of the three Neanderthals studied could have had different reactions to similar stress, illness or diet. This hypothesis is, however, impossible to verify.

As previously indicated, the three considered Neanderthals have similar antiquity, related to MIS 4 (both European fossils) or the beginning of MIS 3 (Kebara 2), meaning that they lived in a cold and dry environment, undoubtedly colder in Europe than in the Levant. In the case of Combe-Grenal level 25, where the studied fossil was found, sedimentological analyses indicate very cold and dry weather conditions with an open Arctic milieu fauna [5]. Layer 11 from Subalyuk Cave displayed similar conditions, according to Bartucz et al. [26]. In Kebara Cave, the analyses demonstrated that Unit XII (where the burial of the individual Kebara 2 appeared) was formed in a dry and cold environment, but not as extreme as that known for the European sites [27].