Scale morphology and squamation play an important role in the study of fish phylogeny and classification. However, as the scales of the earliest osteichthyans or bony fishes are usually found in a disarticulated state, research into squamation patterns and phylogeny has been limited. Here we quantitatively describe the scale morphology of the oldest articulated osteichthyan, the 425-million-year-old Guiyu oneiros, based on geometric morphometrics and high-resolution computed tomography. Based on the cluster analysis of the scales in the articulated specimens, we present a squamation pattern of Guiyu oneiros, which divides the body scales into 4 main belts, comprising 16 areas. The new pattern reveals that the squamation of early osteichthyans is more complicated than previously known, and demonstrates that the taxa near the crown osteichthyan node in late Silurian had a greater degree of squamation zonation compared to more advanced forms. This study offers an important reference for the classification of detached scales of early osteichthyans, provides new insights into the early evolution of osteichthyan scales, and adds to our understanding of the early osteichthyan body plan.

The Sinacanthida ordo nov. and Mongolepidida are spine-and scale-based taxa whose remains encompass some of the earliest reported fossils of chondrichthyan fish. Investigation of fragmentary material from the Early Silurian Tataertag and Ymogantau Formations of the Tarim Basin (Xinjiang Uygur Autonomous Region, China) has revealed a diverse mongolepidid and sinacanthid fauna dominated by mongolepids and sinacanthids in association with abundant dermoskeletal elements of the endemic 'armoured' agnathans known as galeaspids. Micro-computed tomography, scanning electron microscopy and histological sections were used to identify seven mongolepid genera (including Tielikewatielepis sinensis gen. et sp. nov., Xiaohaizilepis liui gen. et sp. nov. and Taklamakanolepis asiaticus gen. et sp. nov.) together with a new chondrichthyan (Yuanolepis bachunensis gen. et sp. nov.) with scale crowns consisting of a mongolepid-type atubular dentine (lamellin). Unlike the more elaborate crown architecture of mongolepids, Yuanolepis gen. nov. exhibits a single row of crown elements consistent with the condition reported in stem chondrichthyans from the Lower Devonian (e.g. in Seretolepis, Parexus). The results corroborate previous work by recognising lamellin as the main component of sinacanthid spines and point to corresponding developmental patterns shared across the dermal skeleton of taxa with lamellin and more derived chondrichthyans (e.g. Doliodus, Kathemacanthus, Seretolepis and Parexus). The Tarim mongolepid fauna is inclusive of coeval taxa from the South China Block and accounts for over two-thirds of the species currently attributed to Mongolepidida. This demonstrates considerable overlap between the Tarim and South China components of the Lower Silurian Zhangjiajie Vertebrate Fauna.

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named 'Archives of Digital Morphology' (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.INDEX TERMS Archives of Digital Morphology, data preprocessing, feature extraction, 3D microfossil model classification, deep learning.

No abstract

Innovations relating to the consumption of hard prey are implicated in ecological shifts in marine ecosystems as early as the mid-Paleozoic. Lungfishes represent the first and longest-ranging lineage of durophagous vertebrates, but how and when the various feeding specializations of this group arose remain unclear. Two exceptionally preserved fossils of the Early Devonian lobe-finned fish Youngolepis reveal the origin of the specialized lungfish feeding mechanism. Youngolepis has a radically restructured palate, reorienting jaw muscles for optimal force transition, coupled with radiating entopterygoid tooth rows like those of lungfish toothplates. This triturating surface occurs in conjunction with marginal dentition and blunt coronoid fangs, suggesting a role in crushing rather than piercing prey. Bayesian tip-dating analyses incorporating these morphological data indicate that the complete suite of lungfish feeding specializations may have arisen in as little as 7 million years, representing one of the most striking episodes of innovation during the initial evolutionary radiations of bony fishes.

No abstract

Abstract. Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.

No abstract