Mesozoic fishes are extremely diverse. In fact, fishes are the most diverse group of vertebrates during the Mesozoic─just as during any other era. Yet, their study is severely underrepresented in comparison to other fossil groups. There are just too few palaeoichthyologists to deal with such a vast diversity of fishes. Nonetheless, thanks to the huge efforts they have made over the last few decades, we have come a long way in our understanding of Mesozoic ichthyofaunas. One of such devoted palaeoichthyologists is Dr. Adriana López-Arbarello, whose contributions have been crucial in elucidating the phylogenetic interrelationships and taxonomic diversity of Mesozoic actinopterygian fishes (e.g., López-Arbarello, 2012; López-Arbarello & Sferco, 2018; López-Arbarello & Ebert, 2023). In her most recent manuscript, Dr. López-Arbarello has joined forces with Dr. Rainer Brocke to tackle the taxonomy and systematics of the halecomorph fishes from one of the most relevant Triassic sites, the upper Ladinian Perledo locality from Italy (López-Arbarello & Brocke, 2024). 

Fossil fishes were reported for the first time from Perledo in the first half of the 19th century (Balsamo-Crivelli, 1839), and up to 30 different species were described from the locality in the subsequent decades. Unfortunately, this is one of the multiple examples of fossil collections that suffered the effects of World War II, and most of the type material was lost. As a consequence, many of those 30 species that have been described over the years are in need of a revision. Based on the study of additional material that was transferred to Germany and is housed at the Senckenberg Research Institute and Natural History Museum, López-Arbarello & Brocke (2024) confirm the presence of four different species of halecomorph fishes in Perledo, which were previously put under synonymy (Lombardo, 2001). They provide new detailed information on the anatomy of two of those species, together with their respective diagnoses. But more importantly, they carry out a thorough exercise of taxonomy, rigorously applying the International Code of Zoological Nomenclature to disentangle the intricacies in the taxonomic story of the species placed in the genus Allolepidotus. As a result, they propose the presence of the species A. ruppelii, which they propose to be the type species for that genus (instead of A. bellottii, which they transfer to the genus Eoeugnathus). They also propose a new genus for the other species originally included in Allolepidotus, A. nothosomoides. Finally, they provide a set of measurements and ratios for Pholidophorus oblongus and Pholidophorus curionii, the other two species previously put in synonymy with A. bellottii, to demonstrate their validity as different species. However, due to the loss of the type material, the authors propose that these two species remain as nomina dubia. 

In summary, apart from providing new detailed anatomical descriptions of two species and solving some long-standing issues with the taxonomy of the halecomorphs from the relevant Triassic Perledo locality, the paper by López-Arbarello & Rainer (2024) highlights three important topics for the study of the fossil record: 1) we should never forget that world-scale problems, such as World Wars, also affect our capacity to understand the natural world in which we live, and the whole society should be aware if this; 2) the importance of exhaustively following the International Code of Zoological Nomenclature when describing new species; and 3) we are in need of new palaeoichthyologists to, in Dr. López-Arbarello’s own words, “unveil the mysteries of those marvellous Mesozoic ichthyofaunas.”

References

Balsamo-Crivelli, G. (1839). Descrizione di un nuovo rettile fossile, della famiglia dei Paleosauri, e di due pesci fossili, trovati nel calcare nero, sopra Varenna sul lago di Como, dal nobile sig. Ludovico Trotti, con alcune riflessioni geologiche. Il politecnico repertorio mensile di studj applicati alla prosperita e coltura sociale, 1, 421–431.

Lombardo, C. (2001). Actinopterygians from the Middle Triassic of northern Italy and Canton Ticino (Switzerland): Anatomical descriptions and nomenclatural problems. Rivista Italiana di Paleontologia e Stratigrafia, 107, 345–369. https://doi.org/10.13130/2039-4942/5439

López-Arbarello, A. (2012). Phylogenetic interrelationships of ginglymodian fishes (Actinopterygii: Neopterygii). PLOS ONE, 7(7), e39370. https://doi.org/10.1371/journal.pone.0039370

López-Arbarello, A., and Brocke, R. (2024). New generic name for a small Triassic ray-finned fish from Perledo (Italy). PaleorXiv, bxmg5, ver. 4, peer-reviewed by PCI Paleo. https://doi.org/10.31233/osf.io/bxmg5

López-Arbarello, A., and Ebert, M. (2023). Taxonomic status of the caturid genera (Halecomorphi, Caturidae) and their Late Jurassic species. Royal Society Open Science, 10(1), 221318. https://doi.org/10.1098/rsos.221318

López-Arbarello, A., and Sferco, E. (2018). Neopterygian phylogeny: The merger assay. Royal Society Open Science, 5(3), 172337. https://doi.org/10.1098/rsos.172337

Bedane et al. (2024) provide a beautifully illustrated demonstration of the difficulties in using dental adaptation as proxies for the diets of elephants, which are in turn often used to determine the vegetation in an area. This study set out to assess mesowear, which is the relief on the teeth that forms due to abrasion by food, and therefore a good proxy of dietary composition in herbivores. The team was interested in testing whether this mesowear relates to morphological adaptations of hypsodonty (high-crownedness) and enamel thickness over a period of ~3.4–~1.1 million years in an elephant species (Elephas recki) commonly found in the Plio/Pleistocene of the Shungura formation (Omo Valley, Ethiopia). To answer this question, the team scored these metrics in 140 molars between ~3.4 and ~1.1 million years of age, separated into time bins. Their results show surprisingly low levels of variation in mesowear, indicating relatively low variation in diet that was overall mostly composed of graze (as opposed to mixed or browsing diets, which are softer). Hypsodonty and enamel thickness were correlated, but changed erratically rather than suggesting a trend towards a particular dietary adaptation. The exciting conclusion is that dental morphologies that we often consider to be adaptive to certain conditions are very slow to evolve, and that a wide variety of morphologies can support the survival of a species despite little variation in diet. For me as a functional evolutionary morphologist, this clear case of many-to-one-mapping is a timely reminder that evolution does not work either quickly or just on the one character complex I might be considering. And in terms of using elephant teeth as ecological proxies – this job clearly just got a little harder.

References

Bedane, T. G., Mackaye, H. T., and Boisserie, J.-R. (2025). New data on morphological evolution and dietary adaptations of Elephas recki from the Plio-Pleistocene Shungura Formation (Lower Omo Valley, Ethiopia). PaleorXiv, qexuf, ver. 4, peer-reviewed by PCI Paleo. https://doi.org/10.31233/osf.io/qexuf

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