Fossils get around. Any one fossil locality might be sampled by several collectors from as many institutions around the world. Alternatively, a single collector might heavily sample a site, and sell or trade parts of their collection to other institutions, scattering the fossils far and wide. These practices have the advantage of making fossils from any one locality available to researchers across the globe. However, they also have the disadvantage that, in order to systematically survey any one species, a researcher must follow innumerable trails of breadcrumb to get to where the relevant materials are held.

This is true of many famous fossil localities, such as the Eocene Green River Formation in the USA, the Cretaceous Kem Kem beds of Morocco, or the Devonian Miguasha cliffs of Canada. It is especially true of the Upper Jurassic deposits of the Morrison Formation in the western USA, which have yielded an impressive assemblage of megaherbivorous sauropod dinosaurs over the last 150 years. Today, these bones are to be found in museums not just in the USA, but also in Canada, Argentina, Japan, Australia, Malaysia, South Africa, and throughout Europe. Trawling museum databases in search of sauropod material from the Morrison Formation can therefore be a daunting task, never mind traveling the globe to actually study them.

A new paper by Tschopp et al. (2019) seeks to ease the burden on sauropod researchers by introducing a database of Morrison Formation sauropods, consisting of over 3000 specimens housed in nearly 40 institutions around the world. The authors are themselves sauropod workers and, having suffered first-hand the plight of studying material from the Morrison Formation, came up with a solution to the problem of keeping track of it all. The database is founded largely on material personally seen by the authors, supplemented by information from the literature and museum catalogs. The database further provides information on bone representation, ontogeny, locality details, and fine-scale stratigraphy, among other fields. Like any database, it is a living document that will continue to grow as new finds are made. Tschopp et al. (2019) have wisely chosen to allow others to contribute to the listing, but changes must first be vetted for accuracy. This product represents 10 years of work, and I have little doubt that it will be well-received by those of us who work on dinosaurs. Speaking personally, my PhD research on megaherbivorous dinosaurs from the Dinosaur Park Formation of Canada led me to institutions in Canada, the USA, and the UK, and further stops to Spain and Argentina would have been beneficial, if affordable. Planning for this work would have been greatly assisted by a database like the one provided us by Tschopp et al. (2019). Many a future graduate student will undoubtedly owe them a debt of gratitude.

References

Tschopp, E., Whitlock, J. A., Woodruff, D. C., Foster, J. R., Lei, R., & Giovanardi, S. (2019). The Morrison Formation Sauropod Consensus: A freely accessible online spreadsheet of collected sauropod specimens, their housing institutions, contents, references, localities, and other potentially useful information. PaleorXiv, version 3, peer-reviewed by PCI Paleo. doi: 10.31233/osf.io/ydvra

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

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

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