Tackling taxonomic and taphonomic biases in the Early Triassic fossil fish record

Cavicchini, Iacopo ORCID: 0000-0002-5814-5662 (2025). Tackling taxonomic and taphonomic biases in the Early Triassic fossil fish record. University of Birmingham. Ph.D.

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Abstract

Actinopterygians survived four of the Phanerozoic main extinctions, including the Permo-Triassic mass extinction (PTME), 252 million years ago. Neopterygians, a clade including most extant fish, appeared in the late Permian and became widespread in the Triassic, thus surviving the PTME. However, definitive estimations of the impact the extinction had on fish are lacking, despite thousands of exquisite 3D fossils from the Early Triassic. This uncertainty is due to weak phylogenetic hypotheses pertaining to late Permian-Early Triassic actinopterygian evolution, and limited knowledge of the taphonomic biases potentially affecting the Early Triassic fossil record of the clade. In this thesis, micro-computed tomography (XCT scanning) is the main tool used to obtain new taxonomic data and develop new taphonomic methods to help reduce the uncertainties. For taxonomy, a poorly known but well-preserved species from the Early Triassic, Pteronisculus gunnari, is redescribed from tomograms. The results add to our knowledge of the anatomical diversity and ontogeny of non-neopterygian fish, as well as suggesting some Early Triassic fish are the youngest representatives of Carboniferous radiations. In taphonomy, XCT scanning is tested as a data collection method in decay experiments. The results show that it is possible to gather data from carcasses under sediment, even visualising the internal anatomy if aided by staining agents, but additional work is required to conduct an entire experiment non-destructively. Together, the results highlight how applying novel approaches to old specimens and established procedures, can improve our knowledge and contribute to solving uncertain aspects of Early Triassic actinopterygian evolution and preservation.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):