This interesting publication by Suchéras-Marx et al. (2020) highlights peculiar aspects of geochemistry in nannofossils, specifically coccolithophorids. One of the main application of geochemistry on fossil shells is to get hints on the physiology of such extinct taxa. Here, the authors try to get information on the calcification mechanism and processes in Jurassic coccoliths. Coccoliths build a test made of calcium carbonate and one of the most common geochemical proxies used for this fossil group is the Sr/Ca ratio. This isotopic ratio has good chances to be successfully used as a robust proxy for paleoenvironmental reconstruction, but, concerning Jurassic coccoliths things seem to be not straightforward.

The authors managed to compare the isotopic value of Sr/Ca measured on Jurassic coccoliths from different taxonomic groups: the murolith Crepidolithus crassus and the placoliths Watznaueria contracta and Discorhabdus striatus. The results they got clearly show that the Sr/Ca ratio cannot be used as a universal proxy because these species exhibit very different values despite coming from the same stratigraphic level and having undergone minimal diagenetic modification. Data seem to point to a Sr/Ca ratio up to 10 times higher in the murolith species than in the placolith taxa (Suchéras-Marx et al., 2020). One of the explanation given here takes advantage of modern coccolith data and hints to specific polysaccharides that would control the growth of the long R unit in the murolith species. As always, there is plenty of space for additional research, possibly on modern taxa, to sort out the scientific questions that arise from this work.

References

Suchéras-Marx, B., Giraud, F., Simionovici, A., Tucoulou, R., & Daniel, I. (2020). Evidence of high Sr/Ca in a Middle Jurassic murolith coccolith species. PaleorXiv, dcfuq, version 7, peer-reviewed by PCI Paleo. doi: 10.31233/osf.io/dcfuq

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.

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