Rhinocerotoidea originated in the Lower Eocene and diversified well during the Cenozoic in Eurasia, North America and Africa. This taxon encompasses a great diversity of ecologies and body proportions and masses. Within this group, the family Rhinocerotidae, which is the only one with extant representatives, appeared in the Late Eocene (Prothero & Schoch, 1989). They were well diversified during the Early and Middle Miocene, whereas they began to decline in both diversity and geographical range after the Miocene, throughout the Pliocene and Pleistocene, in conjunction with the marked climatic changes (Cerdeño, 1998). 

In Eurasian Early and Middle Miocene fossil localities, a variety of species are often associated. Therefore, it may be quite difficult to estimate how these large herbivores cohabited and whether competition for food resources is reflected in a diversity of ecological niches. The ecologies of these large mammals are rather poorly known and the detailed study of their teeth could bring new elements of answer. Indeed, if teeth carry a strong phylogenetic signal in mammals, they are also of great interest for ecological studies, and they have the additional advantage of being often numerous in the fossil record. 

Hullot et al. (2022) analysed both dental microwear texture, as an indicator of dietary preferences, and enamel hypoplasia, to identify stress sensitivity, in a large number of rhinocerotid fossil teeth from nine Neogene (Early to Middle Miocene) localities in Europe and Pakistan. Their aim was to analyse whether fossil species diversity is associated with a diversity of ecologies, and to investigate possible ecological differences between regions and time periods in relation to climate change. Their results show clear differences in time and space between and within species, and suggest that more flexible species are less vulnerable to environmental stressors. 

Very few studies focus on the palaeocology of Miocene rhinos. This study is therefore a great contribution to the understanding of the evolution of this group.

References

Cerdeño, E. (1998). Diversity and evolutionary trends of the Family Rhinocerotidae (Perissodactyla). Palaeogeography, Palaeoclimatology, Palaeoecology, 141, 13–34. https://doi.org/10.1016/S0031-0182(98)00003-0

Hullot, M., Merceron, G., and Antoine, P.-O. (2022). Spatio-temporal diversity of dietary preferences and stress sensibilities of early and middle Miocene Rhinocerotidae from Eurasia: Impact of climate changes. BioRxiv, 490903, ver. 4 peer-reviewed by PCI Paleo. https://doi.org/10.1101/2022.05.06.490903

Prothero, D. R., and Schoch, R. M. (1989). The evolution of perissodactyls. New York: Oxford University Press.

The Baenidae form a diverse extinct clade of exclusively North American paracryptodiran turtles known from the Early Cretaceous to the Eocene (Hay, 1908; Gaffney, 1972; Joyce and Lyson, 2015). Their fossil record was recently extended down to the Berriasian-Valanginian (Joyce et al. 2020), but the group probably originates in the Late Jurassic because it is usually retrieved as the sister group of Pleurosternidae in phylogenetic analyses. However, baenids only became abundant during the Late Cretaceous, when they are restricted in distribution to the western United States, Alberta and Saskatchewan (Joyce and Lyson, 2015).

During the Campanian, baenids are abundant in the northern (Alberta, Montana) and southern (Texas, New Mexico, Utah) parts of their range, but in the middle part of this range they are mostly represented by poorly diagnosable shell fragments. In their new contribution, Wu et al. (2023) describe a new articulated baenid specimen from the Campanian Mesaverde Formation of Wyoming. Despite its poor preservation, they are able to confidently assign this partial shell to Neurankylus sp., hence definitively confirming the presence of baenids and Neurankylus in this formation. Incidentally, this new specimen was found in a non-fluvial depositional environment, which would also confirm the interpretation of Neurankylus as a pond turtle (Hutchinson and Archibald, 1986; Sullivan et al., 1988; Wu et al., 2023; see also comments from the second reviewer).

The study of Wu et al. (2023) also includes a detailed account of the state of the fossil when it was discovered and the subsequent extraction and preparation procedures followed by the team. This may seem excessive or out of place to some, but I agree with the authors that such information, when available, should be more commonly integrated into scientific articles describing new fossil specimens. Preparation and restoration can have a significant impact on the perceived morphology. This must be taken into account when working with fossil specimens. The chemicals or products used to treat, prepare, or consolidate the specimens are also important information for long-term curation. Therefore, it is important that such information is recorded and made available for researchers, curators, and preparators.

References

Gaffney, E. S. (1972). The systematics of the North American family Baenidae (Reptilia, Cryptodira). Bulletin of the American Museum of Natural History, 147(5), 241–320.

Hay, O. P. (1908). The Fossil Turtles of North America. Carnegie Institution of Washington, Washington, D.C. https://doi.org/10.5962/bhl.title.12500

Hutchison, J. H., and Archibald, J. D. (1986). Diversity of turtles across the Cretaceous/Tertiary boundary in Northeastern Montana. Palaeogeography, Palaeoclimatology, Palaeoecology, 55(1), 1–22. https://doi.org/10.1016/0031-0182(86)90133-1

Joyce, W. G., and Lyson, T. R. (2015). A review of the fossil record of turtles of the clade Baenidae. Bulletin of the Peabody Museum of Natural History, 56(2), 147–183. https://doi.org/10.3374/014.058.0105

Joyce, W. G., Rollot, Y., and Cifelli, R. L. (2020). A new species of baenid turtle from the Early Cretaceous Lakota Formation of South Dakota. Fossil Record, 23(1), 1–13. https://doi.org/10.5194/fr-23-1-2020

Sullivan, R. M., Lucas, S. G., Hunt, A. P., and Fritts, T. H. (1988). Color pattern on the selmacryptodiran turtle Neurankylus from the Early Paleocene (Puercan) of the San Juan Basin, New Mexico. Contributions in Science, 401, 1–9. https://doi.org/10.5962/p.241286

Wu, K. Y., Heuck, J., Varriale, F. J., and Farke, A. (2023). A baenid turtle shell from the Mesaverde Formation (Campanian, Late Cretaceous) of Park County, Wyoming, USA. PaleorXiv, uk3ac, ver. 5, peer-reviewed and recommended by Peer Community In Paleontology. https://doi.org/10.31233/osf.io/uk3ac

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

There are less and less experts on taxonomy of particular groups particularly among early career paleontologists and (paleo)biologists – this also includes ammonoid cephalopods. Techniques cannot replace this taxonomic expertise (Engel et al. 2021) but machine learning approaches can make taxonomy more efficient, reproducible as well as passing it over more sustainable. Initially ammonoid taxonomy was a black box with small differences sometimes sufficient to erect different species as well as really idiosyncratic groupings of superficially similar specimens (see De Baets et al. 2015 for a review). In the meantime, scientists have embraced more quantitative assessments of conch shape and morphology more generally (see Klug et al. 2015 for a more recent review). The approaches still rely on important but time-intensive collection work and seeing through daisy chains of more or less accessible papers and monographs without really knowing how these approaches perform (other than expert opinion). In addition, younger scientists are usually trained by more experienced scientists, but this practice is becoming more and more difficult which makes it difficult to resolve the taxonomic gap. This relates to the fact that less and less experienced researchers with this kind of expertise get employed as well as graduate students or postdocs choosing different research or job avenues after their initial training effectively leading to a leaky pipeline and taxonomic impediment.

Robust taxonomy and stratigraphy is the basis for all other studies we do as paleontologists/paleobiologists so Foxon (2021) represents the first step to use supervised and unsupervised machine-learning approaches and test their efficiency on ammonoid conch properties. This pilot study demonstrates that machine learning approaches can be reasonably accurate (60-70%) in identifying ammonoid species (Foxon, 2021) – at least similar to that in other mollusk taxa (e.g., Klinkenbuß et al. 2020) - and might also be interesting to assist in cases where more traditional methods are not feasible. Novel approaches might even allow to further approve the accuracy as has been demonstrated for other research objects like pollen (Romero et al. 2020). Further applying of machine learning approaches on larger datasets and additional morphological features (e.g., suture line) are now necessary in order to test and improve the robustness of these approaches for ammonoids as well as test their performance more broadly within paleontology.

References

De Baets K, Bert D, Hoffmann R, Monnet C, Yacobucci M, and Klug C (2015). Ammonoid intraspecific variability. In: Ammonoid Paleobiology: From anatomy to ecology. Ed. by Klug C, Korn D, De Baets K, Kruta I, and Mapes R. Vol. 43. Topics in Geobiology. Dordrecht: Springer, pp. 359–426.

Engel MS, Ceríaco LMP, Daniel GM, Dellapé PM, Löbl I, Marinov M, Reis RE, Young MT, Dubois A, Agarwal I, Lehmann A. P, Alvarado M, Alvarez N, Andreone F, Araujo-Vieira K, Ascher JS, Baêta D, Baldo D, Bandeira SA, Barden P, Barrasso DA, Bendifallah L, Bockmann FA, Böhme W, Borkent A, Brandão CRF, Busack SD, Bybee SM, Channing A, Chatzimanolis S, Christenhusz MJM, Crisci JV, D’elía G, Da Costa LM, Davis SR, De Lucena CAS, Deuve T, Fernandes Elizalde S, Faivovich J, Farooq H, Ferguson AW, Gippoliti S, Gonçalves FMP, Gonzalez VH, Greenbaum E, Hinojosa-Díaz IA, Ineich I, Jiang J, Kahono S, Kury AB, Lucinda PHF, Lynch JD, Malécot V, Marques MP, Marris JWM, Mckellar RC, Mendes LF, Nihei SS, Nishikawa K, Ohler A, Orrico VGD, Ota H, Paiva J, Parrinha D, Pauwels OSG, Pereyra MO, Pestana LB, Pinheiro PDP, Prendini L, Prokop J, Rasmussen C, Rödel MO, Rodrigues MT, Rodríguez SM, Salatnaya H, Sampaio Í, Sánchez-García A, Shebl MA, Santos BS, Solórzano-Kraemer MM, Sousa ACA, Stoev P, Teta P, Trape JF, Dos Santos CVD, Vasudevan K, Vink CJ, Vogel G, Wagner P, Wappler T, Ware JL, Wedmann S, and Zacharie CK (2021). The taxonomic impediment: a shortage of taxonomists, not the lack of technical approaches. Zoological Journal of the Linnean Society 193, 381–387. doi: 10. 1093/zoolinnean/zlab072

Foxon F (2021). Ammonoid taxonomy with supervised and unsupervised machine learning algorithms. PaleorXiv ewkx9, ver. 3, peer-reviewed by PCI Paleo. doi: 10.31233/osf.io/ewkx9

Klinkenbuß D, Metz O, Reichert J, Hauffe T, Neubauer TA, Wesselingh FP, and Wilke T (2020). Performance of 3D morphological methods in the machine learning assisted classification of closely related fossil bivalve species of the genus Dreissena. Malacologia 63, 95. doi: 10.4002/040.063.0109

Klug C, Korn D, Landman NH, Tanabe K, De Baets K, and Naglik C (2015). Ammonoid conchs. In: Ammonoid Paleobiology: From anatomy to ecology. Ed. by Klug C, Korn D, De Baets K, Kruta I, and Mapes RH. Vol. 43. Dordrecht: Springer, pp. 3–24.

Romero IC, Kong S, Fowlkes CC, Jaramillo C, Urban MA, Oboh-Ikuenobe F, D’Apolito C, and Punyasena SW (2020). Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy. Proceedings of the National Academy of Sciences 117, 28496–28505. doi: 10.1073/pnas.2007324117