Three-dimensional microwear analysis is a very potent method in capturing the diet and, thus, reconstructing trophic relationships. It is widely applied in archaeology, palaeontology, neontology and (palaeo)anthropology. The method had been developed for mammal teeth (Walker et al., 1978; Teaford, 1988; Calandra and Merceron, 2016), but it has proven to be applicable to sharks (McLennan and Purnell, 2021) and reptiles, including fossil taxa with rather mysterious trophic ecologies (e.g., Bestwick et al., 2020; Holwerda et al., 2023). Microwear analysis has brought about landmark discoveries extending beyond autecology and reaching into palaeoenvironmental reconstructions (e.g., Merceron et al., 2016), niche evolution (e.g., Thiery et al., 2021), and assessment of food availability and niche partitioning (Ősi et al., 2022). Furthermore, microwear analysis is a testable method, which can be investigated experimentally in extant animals in order to ground-truth dietary interpretations in extinct organisms. 

The study by Thiery et al. (2024) addresses important limitations of 3D microwear analysis: 1) the unequal access to commercial software required to analyze surface data obtained using confocal profilometers; 2) lack of replicability resulting from the use of commercial software with graphical user interface only. The latter point results in that documenting precisely what has been analyzed and how is nearly impossible.

The use of algorithms such as scale-sensitive fractal analysis (Ungar et al., 2003; Scott et al., 2006) and surface texture analysis has greatly improved replicability of DMTA and nearly eliminated intra- and inter-observer errors. Substantial effort has been made to quantify and minimize systematic and random errors in microwear analyses, such as intraspecific variation, use of different equipment (Arman et al., 2016), use of casts (Mihlbachler et al., 2019) or non-dietary variables (Bestwick et al., 2021). But even the best designed study cannot be replicated if the analysis is carried out with a “black box” software that many researchers may not afford. The trident package for R Software (https://github.com/nialsiG/trident) presented by Thiery et al. (2024) allows users to calculate 24 variables used in DMTA, transform them, calculate their variation across a surface, and rank them according to a sophisticated workflow that takes into account their normality and heteroscedasticity. A graphical user interface (GUI) is included in the form of a ShinyApp, but the power of the package, in my opinion, lies in that all steps of the analyses can be saved as R code and shared together with a study. This is a fundamental contribution to replicability and validation of microwear analyses. As best practices in code quality and replication become better known and accessible to palaeobiologists (The Turing Way Community, 2022; Trisovic et al., 2022). The presentation of the trident package is associated with three case studies, each with associated instructions on reproducing the results. These instructions partly use the literate programming approach, so that each step of the analysis is discussed and the methods are presented, either as screen shots when the GUI is used, or code. This is an excellent contribution, which hopefully will be followed by future microwear studies.

References

Arman, S. D., Ungar, P. S., Brown, C. A., DeSantis, L. R. G., Schmidt, C., and Prideaux, G. J. (2016). Minimizing inter-microscope variability in dental microwear texture analysis. Surface Topography: Metrology and Properties, 4(2), 024007. https://doi.org/10.1088/2051-672X/4/2/024007

Bestwick, J., Unwin, D. M., Butler, R. J., and Purnell, M. A. (2020). Dietary diversity and evolution of the earliest flying vertebrates revealed by dental microwear texture analysis. Nature Communications, 11(1), 5293. https://doi.org/10.1038/s41467-020-19022-2

Bestwick, J., Unwin, D. M., Henderson, D. M., and Purnell, M. A. (2021). Dental microwear texture analysis along reptile tooth rows: Complex variation with non-dietary variables. Royal Society Open Science, 8(2), 201754. https://doi.org/10.1098/rsos.201754

Calandra, I., and Merceron, G. (2016). Dental microwear texture analysis in mammalian ecology. Mammal Review, 46(3), 215–228. https://doi.org/10.1111/mam.12063

Holwerda, F. M., Bestwick, J., Purnell, M. A., Jagt, J. W. M., and Schulp, A. S. (2023). Three-dimensional dental microwear in type-Maastrichtian mosasaur teeth (Reptilia, Squamata). Scientific Reports, 13(1), 18720. https://doi.org/10.1038/s41598-023-42369-7

McLennan, L. J., and Purnell, M. A. (2021). Dental microwear texture analysis as a tool for dietary discrimination in elasmobranchs. Scientific Reports, 11(1), 2444. https://doi.org/10.1038/s41598-021-81258-9

Merceron, G., Novello, A., and Scott, R. S. (2016). Paleoenvironments inferred from phytoliths and Dental Microwear Texture Analyses of meso-herbivores. Geobios, 49(1–2), 135–146. https://doi.org/10.1016/j.geobios.2016.01.004

Mihlbachler, M. C., Foy, M., and Beatty, B. L. (2019). Surface replication, fidelity and data loss in traditional dental microwear and dental microwear texture analysis. Scientific Reports, 9(1), 1595. https://doi.org/10.1038/s41598-018-37682-5

Ősi, A., Barrett, P. M., Evans, A. R., Nagy, A. L., Szenti, I., Kukovecz, Á., Magyar, J., Segesdi, M., Gere, K., and Jó, V. (2022). Multi-proxy dentition analyses reveal niche partitioning between sympatric herbivorous dinosaurs. Scientific Reports, 12(1), 20813. https://doi.org/10.1038/s41598-022-24816-z

Scott, R. S., Ungar, P. S., Bergstrom, T. S., Brown, C. A., Childs, B. E., Teaford, M. F., and Walker, A. (2006). Dental microwear texture analysis: Technical considerations. Journal of Human Evolution, 51(4), 339–349. https://doi.org/10.1016/j.jhevol.2006.04.006

Teaford, M. F. (1988). A review of dental microwear and diet in modern mammals. Scanning Microscopy, 2, 1149–1166.

The Turing Way Community. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research (Version 1.0.2). Zenodo. https://doi.org/10.5281/ZENODO.3233853

Thiery, G., Francisco, A., Louail, M., Berlioz, É., Blondel, C., Brunetière, N., Ramdarshan, A., Walker, A. E. C., and Merceron, G. (2024). Introducing “trident”: A graphical interface for discriminating groups using dental microwear texture analysis. HAL, hal-04222508, ver. 4 peer-reviewed by PCI Paleo. https://hal.science/hal-04222508v4

Thiery, G., Gibert, C., Guy, F., Lazzari, V., Geraads, D., Spassov, N., and Merceron, G. (2021). From leaves to seeds? The dietary shift in late Miocene colobine monkeys of southeastern Europe. Evolution, 75(8), 1983–1997. https://doi.org/10.1111/evo.14283

Trisovic, A., Lau, M. K., Pasquier, T., and Crosas, M. (2022). A large-scale study on research code quality and execution. Scientific Data, 9(1), 60. https://doi.org/10.1038/s41597-022-01143-6

Ungar, P. S., Brown, C. A., Bergstrom, T. S., and Walker, A. (2003). Quantification of dental microwear by tandem scanning confocal microscopy and scale‐sensitive fractal analyses. Scanning, 25(4), 185–193. https://doi.org/10.1002/sca.4950250405

Walker, A., Hoeck, H. N., and Perez, L. (1978). Microwear of mammalian teeth as an indicator of diet. Science, 201(4359), 908–910. https://doi.org/10.1126/science.684415

A fundamental question in evolutionary biology and paleobiology is how quickly populations and/or species evolve and under what circumstances. Because the fossil record affords us the most direct view of how species lineages have changed in the past, considerable effort has gone into developing methodological approaches for assessing rates of evolution as well as what has been termed “mode of evolution” which generally describes pattern of evolution, for example whether the morphological change captured in fossil time series are best characterized as static, punctuated, or trending (e.g., Sheets and Mitchell, 2001; Hunt, 2006, 2008; Voje et al., 2018). The rock record from which these samples are taken, however, is incomplete, due to spatially and temporally heterogenous sediment deposition and erosion. The resulting structure of the stratigraphic record may confound the direct application of evolutionary models to fossil time series, most of which come from single localities. This pressing issue is tackled in a new study entitled “Identification of the mode of evolution in incomplete carbonate successions” (Hohmann et al., 2024).

The “carbonate successions” part of the title is important. Previous similar work (Hannisdal, 2006) used models for siliciclastic depositional systems to simulate the rock record. Here, the authors simulate sediment deposition across a carbonate platform, a system that has been treated as fundamentally different from siliciclastic settings, from both the point of view of geology (see Wagoner et al., 1990; Schlager, 2005) and ecology (Hopkins et al., 2014). The authors do find that stratigraphic structure impacts the identification of mode of evolution but not necessarily in the way one might expect; specifically, it is less important how much time is represented compared to the size and distribution of gaps, regardless of where you are sampling along the platform. This result provides an important guiding principle for selecting fossil time series for future investigations. 

Another very useful result of this study is the impact of time series length, which in this case should be understood as the density of sampling over a particular time interval. Counterintuitively, the probability of selecting the data-generating model as the best model decreases with increased length. The authors propose several explanations for this, all of which should inspire further work. There are also many other variables that could be explored in the simulations of the carbonate models as well as the fossil time series. For example, the authors chose to minimize within-sample variation in order to avoid conflating variability with evolutionary trends. But greater variance also potentially impacts model selection results and underlies questions about how variation relates to evolvability and the potential for directional change. 

Lastly, readers of Hohmann et al. (2024) are encouraged to also peruse the reviews and author replies associated with the PCI Paleo peer review process. The discussion contained in these documents touch on several important topics, including model performance and model selection, the nature of nested model systems, and the potential of the forwarding modeling approach.  

References

Hannisdal, B. (2006). Phenotypic evolution in the fossil record: Numerical experiments. The Journal of Geology, 114(2), 133–153. https://doi.org/10.1086/499569

Hohmann, N., Koelewijn, J. R., Burgess, P., and Jarochowska, E. (2024). Identification of the mode of evolution in incomplete carbonate successions. bioRxiv, 572098, ver. 4 peer-reviewed by PCI Paleo. https://doi.org/10.1101/2023.12.18.572098

Hopkins, M. J., Simpson, C., and Kiessling, W. (2014). Differential niche dynamics among major marine invertebrate clades. Ecology Letters, 17(3), 314–323. https://doi.org/10.1111/ele.12232

Hunt, G. (2006). Fitting and comparing models of phyletic evolution: Random walks and beyond. Paleobiology, 32(4), 578–601. https://doi.org/10.1666/05070.1

Hunt, G. (2008). Gradual or pulsed evolution: When should punctuational explanations be preferred? Paleobiology, 34(3), 360–377. https://doi.org/10.1666/07073.1

Schlager, W. (Ed.). (2005). Carbonate Sedimentology and Sequence Stratigraphy. SEPM Concepts in Sedimentology and Paleontology No. 8. https://doi.org/10.2110/csp.05.08

Sheets, H. D., and Mitchell, C. E. (2001). Why the null matters: Statistical tests, random walks and evolution. Genetica, 112, 105–125. https://doi.org/10.1023/A:1013308409951

Voje, K. L., Starrfelt, J., and Liow, L. H. (2018). Model adequacy and microevolutionary explanations for stasis in the fossil record. The American Naturalist, 191(4), 509–523. https://doi.org/10.1086/696265

Wagoner, J. C. V., Mitchum, R. M., Campion, K. M., and Rahmanian, V. D. (1990). Siliciclastic Sequence Stratigraphy in Well Logs, Cores, and Outcrops: Concepts for High-Resolution Correlation of Time and Facies. AAPG Methods in Exploration Series No. 7. https://doi.org/10.1306/Mth7510

Suidae are well-represented in Plio-Pleistocene African hominin sites and are particularly important for biochronological assessments. Their ubiquity in hominin sites combined with multiple appearances of what appears to be graminivorous adaptations in the lineage (Harris & White, 1979) suggest that they have the potential to contribute to our understanding of Plio-Pleistocene paleoenvironments. While they have been generally understudied in this respect, there has been recent focus on their diets to understand the paleoenvironments of early hominin habitats. Of particular interest is Kolpochoerus, one of the most abundant suid genera in the Plio-Pleistocene with a wide geographic distribution and diverse dental morphologies (Harris & White, 1979). 

In this study, Louail et al. (2024) present the results of the first dental microwear texture analysis (DMTA) conducted on suids from the Shungura Formation of the Omo Valley, an important Plio-Pleistocene hominin site that records an almost continuous sedimentary record from ca. 3.75 Ma to 1.0 Ma (Heinzelin 1983; McDougall et al., 2012; Kidane et al., 2014). Dental microwear is one of the main proxies in understanding diet in fossil mammals, particularly herbivores, and DMTA has been shown to be effective in differentiating inter- and intra-species dietary differences (e.g., Scott et al., 2006; 2012; Merceron et al., 2010). However, only a few studies have applied this method to extinct suids (Souron et al., 2015; Ungar et al., 2020), making this study especially pertinent for those interested in suid dietary evolution or hominin paleoecology.

In addition to examining DMT variations of Kolpochoerus specimens from Omo, Louail et al. (2024) also expanded the modern comparative data set to include larger samples of African suids with different diets from herbivores to omnivores to better interpret the fossil data. They found that DMTA distinguishes between extant suid taxa, reflecting differences in diet, indicating that DMT can be used to examine the diets of fossil suids. The results suggest that Kolpochoerus at Omo had a substantially different diet from any extant suid taxon and that although its anistropy values increased through time, they remain well below those observed in modern Phacochoerus that specializes in fibrous, abrasive plants. Based on these results, in combination with comparative and experimental DMT, enamel carbon isotopic, and morphological data, Louail et al. (2024) propose that Omo Kolpochoerus preferred short, soft and low abrasive herbaceous plants (e.g., fresh grass shoots), probably in more mesic habitats. Louail et al. (2024) note that with the wide temporal and geographic distribution of Kolpochoerus, different species and populations may have had different feeding habits as they exploited different local habitats. However, it is noteworthy that similar inferences were made at other hominin sites based on other types of dietary data (e.g., Harris & Cerling, 2002; Rannikko et al., 2017, 2020; Yang et al., 2022). If this is an indication of their habitat preferences, the wide-ranging distribution of Kolpochoerus may suggest that mesic habitats with short, soft herbaceous plants were present in various proportions at most Plio-Pleistocene hominin sites. 

References

Harris, J. M., and Cerling, T. E. (2002). Dietary adaptations of extant and Neogene African suids. Journal of Zoology, 256(1), 45–54. https://doi.org/10.1017/S0952836902000067

Harris, J. M., and White, T. D. (1979). Evolution of the Plio-Pleistocene African Suidae. Transactions of the American Philosophical Society, 69(2), 1–128. https://doi.org/10.2307/1006288

Heinzelin, J. de. (1983). The Omo Group. Archives of the International Omo Research Expedition. Volume 85. Annales du Musée Royal de l’Afrique Centrale, série 8, Sciences géologiques, Tervuren, 388 p.

Kidane, T., Brown, F. H., and Kidney, C. (2014). Magnetostratigraphy of the fossil-rich Shungura Formation, southwest Ethiopia. Journal of African Earth Sciences, 97, 207–223. https://doi.org/10.1016/j.jafrearsci.2014.05.005

Louail, M., Souron, A., Merceron, G., and Boisserie, J.-R. (2024). New insights on feeding habits of Kolpochoerus from the Shungura Formation (Lower Omo Valley, Ethiopia) using dental microwear texture analysis. PaleorXiv, dbgtp, ver. 3, peer-reviewed by PCI Paleo. https://doi.org/10.31233/osf.io/dbgtp

McDougall, I., Brown, F. H., Vasconcelos, P. M., Cohen, B. E., Thiede, D. S., and Buchanan, M. J. (2012). New single crystal 40Ar/39Ar ages improve time scale for deposition of the Omo Group, Omo–Turkana Basin, East Africa. Journal of the Geological Society, 169(2), 213–226. https://doi.org/10.1144/0016-76492010-188

Merceron, G., Escarguel, G., Angibault, J.-M., and Verheyden-Tixier, H. (2010). Can dental microwear textures record inter-individual dietary variations? PLoS ONE, 5(3), e9542. https://doi.org/10.1371/journal.pone.0009542

Rannikko, J., Adhikari, H., Karme, A., Žliobaitė, I., and Fortelius, M. (2020). The case of the grass‐eating suids in the Plio‐Pleistocene Turkana Basin: 3D dental topography in relation to diet in extant and fossil pigs. Journal of Morphology, 281(3), 348–364. https://doi.org/10.1002/jmor.21103

Rannikko, J., Žliobaitė, I., and Fortelius, M. (2017). Relative abundances and palaeoecology of four suid genera in the Turkana Basin, Kenya, during the late Miocene to Pleistocene. Palaeogeography, Palaeoclimatology, Palaeoecology, 487, 187–193. https://doi.org/10.1016/j.palaeo.2017.08.033

Scott, R. S., Teaford, M. F., and Ungar, P. S. (2012). Dental microwear texture and anthropoid diets. American Journal of Physical Anthropology, 147(4), 551–579. https://doi.org/10.1002/ajpa.22007

Scott, R. S., Ungar, P. S., Bergstrom, T. S., Brown, C. A., Childs, B. E., Teaford, M. F., and Walker, A. (2006). Dental microwear texture analysis: Technical considerations. Journal of Human Evolution, 51(4), 339–349. https://doi.org/10.1016/j.jhevol.2006.04.006

Souron, A., Merceron, G., Blondel, C., Brunetière, N., Colyn, M., Hofman-Kamińska, E., and Boisserie, J.-R. (2015). Three-dimensional dental microwear texture analysis and diet in extant Suidae (Mammalia: Cetartiodactyla). Mammalia, 79(3). https://doi.org/10.1515/mammalia-2014-0023

Ungar, P. S., Abella, E. F., Burgman, J. H. E., Lazagabaster, I. A., Scott, J. R., Delezene, L. K., Manthi, F. K., Plavcan, J. M., and Ward, C. V. (2020). Dental microwear and Pliocene paleocommunity ecology of bovids, primates, rodents, and suids at Kanapoi. Journal of Human Evolution, 140, 102315. https://doi.org/10.1016/j.jhevol.2017.03.005

Yang, D., Pisano, A., Kolasa, J., Jashashvili, T., Kibii, J., Gomez Cano, A. R., Viriot, L., Grine, F. E., and Souron, A. (2022). Why the long teeth? Morphometric analysis suggests different selective pressures on functional occlusal traits in Plio-Pleistocene African suids. Paleobiology, 48(4), 655–676. https://doi.org/10.1017/pab.2022.11

As for many groups, such as equids or elephants, the number of living rhinoceros species is just a fraction of their past diversity as revealed by the fossil record. Besides being far more widespread and taxonomically diverse, rhinos also came in a greater variety of shapes and sizes. Some of these – teleoceratines, or so-called ‘hippo-like’ rhinos – had short limbs, barrel-shaped bodies, were often hornless, and might have been semi-aquatic (Prothero et al., 1989; Antoine, 2002). Teleoceratines existed from the Oligocene to the Pliocene, and have been recorded from Eurasia, Africa, and North and Central America. Despite this large temporal and spatial presence, large gaps remain in our knowledge of this group, particularly when it comes to their phylogeny and their relationships to other parts of the rhino tree (Antoine, 2002; Lu et al., 2021). Here, Sizov et al. (2024) describe an almost complete skeleton of a teleoceratine found in 2008 on an island in Lake Baikal in eastern Russia. Dating to the Early Miocene, this wonderfully preserved specimen includes the skull and limb bones, which are described and figured in detail, and which indicate assignment to Brachydiceratherium shanwangense, a species otherwise known only from Shandong in eastern China, some 2000 km to the southeast (Wang, 1965; Lu et al., 2021).

The study goes on to present a new phylogenetic analysis of the teleoceratines, the results of which have important implications for the taxonomy of fossil rhinos. Besides confirming the monophyly of Teleoceratina, the phylogeny supports the reassignment of most species previously assigned to Diaceratherium to Brachydiceratherium instead.

In a field that is increasingly dominated by analyses of metadata, Sizov et al. (2024) provide a reminder of the importance of fieldwork for the discovery of fossil remains that, sometimes by virtue of a single specimen, can significantly augment our understanding of the evolution and paleobiogeography of extinct species.

References

Antoine, P.-O. (2002). Phylogénie et évolution des Elasmotheriina (Mammalia, Rhinocerotidae). Mémoires du Muséum National d’Histoire Naturelle, 188, 1–359.

Lu, X., Cerdeño, E., Zheng, X., Wang, S., & Deng, T. (2021). The first Asian skeleton of Diaceratherium from the early Miocene Shanwang Basin (Shandong, China), and implications for its migration route. Journal of Asian Earth Sciences: X, 6, 100074. https://doi.org/10.1016/j.jaesx.2021.100074

Prothero, D. R., Guérin, C., and Manning, E. (1989). The History of the Rhinocerotoidea. In D. R. Prothero and R. M. Schoch (Eds.), The Evolution of Perissodactyls (pp. 322–340). Oxford University Press.

Sizov, A., Klementiev, A., & Antoine, P.-O. (2024). An Early Miocene skeleton of Brachydiceratherium Lavocat, 1951 (Mammalia, Perissodactyla) from the Baikal area, Russia, and a revised phylogeny of Eurasian teleoceratines. bioRxiv, 498987, ver. 6 peer-reviewed by PCI Paleo. https://doi.org/10.1101/2022.07.06.498987

Wang, B. Y. (1965). A new Miocene aceratheriine rhinoceros of Shanwang, Shandong. Vertebrata Palasiatica, 9, 109–112.

One of the most recent technological advances in paleontology enables the characterization of ancient proteins, a new discipline known as palaeoproteomics (Ostrom et al., 2000; Warinner et al., 2022). Palaeoproteomics has superficial similarities with ancient DNA, as both work with ancient molecules, however the former focuses on peptides and the latter on nucleotides. While the study of ancient DNA is more established (e.g., Shapiro et al., 2019), palaeoproteomics is experiencing a rapid diversification of application, from deep time paleontology (e.g., Schroeter et al., 2022) to taxonomic identification of bone fragments (e.g., Douka et al., 2019), and determining genetic sex of ancient individuals (e.g., Lugli et al., 2022). However, as Patramanis et al. (2023) note in this manuscript, tools for analyzing protein sequence data are still in the informal stage, making the application of this methodology a challenge for many new-comers to the discipline, especially those with little bioinformatics expertise.

In the spirit of democratizing the field of palaeoproteomics, Patramanis et al. (2023) developed an open-source pipeline, PaleoProPhyler released under a CC-BY license (https://github.com/johnpatramanis/Proteomic_Pipeline). Here, Patramanis et al. (2023) introduce their workflow designed to facilitate the phylogenetic analysis of ancient proteins. This pipeline is built on the methods from earlier studies probing the phylogenetic relationships of an extinct genus of rhinoceros Stephanorhinus (Cappellini et al., 2019), the large extinct ape Gigantopithecus (Welker et al., 2019), and Homo antecessor (Welker et al., 2020). PaleoProPhyler has three interacting modules that initialize, construct, and analyze an input dataset. The authors provide a demonstration of application, presenting a molecular hominid phyloproteomic tree. 

In order to run some of the analyses within the pipeline, the authors also generated the Hominid Palaeoproteomic Reference Dataset which includes 10,058 protein sequences per individual translated from publicly available whole genomes of extant hominids (orangutans, gorillas, chimpanzees, and humans) as well as some ancient genomes of Neanderthals and Denisovans. This valuable research resource is also publicly available, on Zenodo (Patramanis et al., 2022). 

Three reviewers reported positively about the development of this program, noting its importance in advancing the application of palaeoproteomics more broadly in paleontology.

References

Cappellini, E., Welker, F., Pandolfi, L., Ramos-Madrigal, J., Samodova, D., Rüther, P. L., Fotakis, A. K., Lyon, D., Moreno-Mayar, J. V., Bukhsianidze, M., Rakownikow Jersie-Christensen, R., Mackie, M., Ginolhac, A., Ferring, R., Tappen, M., Palkopoulou, E., Dickinson, M. R., Stafford, T. W., Chan, Y. L., … Willerslev, E. (2019). Early Pleistocene enamel proteome from Dmanisi resolves Stephanorhinus phylogeny. Nature, 574(7776), 103–107. https://doi.org/10.1038/s41586-019-1555-y

Douka, K., Brown, S., Higham, T., Pääbo, S., Derevianko, A., and Shunkov, M. (2019). FINDER project: Collagen fingerprinting (ZooMS) for the identification of new human fossils. Antiquity, 93(367), e1. https://doi.org/10.15184/aqy.2019.3 

Lugli, F., Nava, A., Sorrentino, R., Vazzana, A., Bortolini, E., Oxilia, G., Silvestrini, S., Nannini, N., Bondioli, L., Fewlass, H., Talamo, S., Bard, E., Mancini, L., Müller, W., Romandini, M., and Benazzi, S. (2022). Tracing the mobility of a Late Epigravettian (~ 13 ka) male infant from Grotte di Pradis (Northeastern Italian Prealps) at high-temporal resolution. Scientific Reports, 12(1), 8104. https://doi.org/10.1038/s41598-022-12193-6

Ostrom, P. H., Schall, M., Gandhi, H., Shen, T.-L., Hauschka, P. V., Strahler, J. R., and Gage, D. A. (2000). New strategies for characterizing ancient proteins using matrix-assisted laser desorption ionization mass spectrometry. Geochimica et Cosmochimica Acta, 64(6), 1043–1050. https://doi.org/10.1016/S0016-7037(99)00381-6

Patramanis, I., Ramos-Madrigal, J., Cappellini, E., and Racimo, F. (2022). Hominid Palaeoproteomic Reference Dataset (1.0.1) [dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7333226

Patramanis, I., Ramos-Madrigal, J., Cappellini, E., and Racimo, F. (2023). PaleoProPhyler: A reproducible pipeline for phylogenetic inference using ancient proteins. BioRxiv, 519721, ver. 3 peer-reviewed by PCI Paleo. https://doi.org/10.1101/2022.12.12.519721

Schroeter, E. R., Cleland, T. P., and Schweitzer, M. H. (2022). Deep Time Paleoproteomics: Looking Forward. Journal of Proteome Research, 21(1), 9–19. https://doi.org/10.1021/acs.jproteome.1c00755

Shapiro, B., Barlow, A., Heintzman, P. D., Hofreiter, M., Paijmans, J. L. A., and Soares, A. E. R. (Eds.). (2019). Ancient DNA: Methods and Protocols (2nd ed., Vol. 1963). Humana, New York. https://doi.org/10.1007/978-1-4939-9176-1

Warinner, C., Korzow Richter, K., and Collins, M. J. (2022). Paleoproteomics. Chemical Reviews, 122(16), 13401–13446. https://doi.org/10.1021/acs.chemrev.1c00703

Welker, F., Ramos-Madrigal, J., Gutenbrunner, P., Mackie, M., Tiwary, S., Rakownikow Jersie-Christensen, R., Chiva, C., Dickinson, M. R., Kuhlwilm, M., De Manuel, M., Gelabert, P., Martinón-Torres, M., Margvelashvili, A., Arsuaga, J. L., Carbonell, E., Marques-Bonet, T., Penkman, K., Sabidó, E., Cox, J., … Cappellini, E. (2020). The dental proteome of Homo antecessor. Nature, 580(7802), 235–238. https://doi.org/10.1038/s41586-020-2153-8

Welker, F., Ramos-Madrigal, J., Kuhlwilm, M., Liao, W., Gutenbrunner, P., De Manuel, M., Samodova, D., Mackie, M., Allentoft, M. E., Bacon, A.-M., Collins, M. J., Cox, J., Lalueza-Fox, C., Olsen, J. V., Demeter, F., Wang, W., Marques-Bonet, T., and Cappellini, E. (2019). Enamel proteome shows that Gigantopithecus was an early diverging pongine. Nature, 576(7786), 262–265. https://doi.org/10.1038/s41586-019-1728-8