Can fractal dimensions objectivize gastropod shell morphometrics? A case study from Lake Lugu (SW China)

Abstract

Morphometrics are fundamental for the analysis of size and shape in fossils, particularly because soft parts or DNA are rarely preserved and hard parts such as shells are commonly the only source of information. Geometric morphometrics, that is, landmark analysis, is well established for the description of shape but it exhibits a couple of shortcomings resulting from subjective choices during landmarking (number and position of landmarks) and from difficulties in resolving shape at the level of micro‐sculpture. With the aid of high‐resolution 3D scanning technology and analyses of fractal dimensions, we test whether such shortcomings of linear and landmark morphometrics can be overcome. As a model group, we selected a clade of modern viviparid gastropods from Lake Lugu, with shells that show a high degree of sculptural variation. Linear and landmark analyses were applied to the same shells in order to establish the fractal dimensions. The genetic diversity of the gastropod clade was assessed. The genetic results suggest that the gastropod clade represents a single species. The results of all morphometric methods applied are in line with the genetic results, which is that no specific morphotype could be delimited. Apart from this overall agreement, landmark and fractal dimension analyses do not correspond to each other but represent data sets with different information. Generally, the fractal dimension values quantify the roughness of the shell surface, the resolution of the 3D scans determining the level. In our approach, we captured the micro‐sculpture but not the first‐order sculptural elements, which explains that fractal dimension and landmark data are not in phase. We can show that analyzing fractal dimensions of gastropod shells opens a window to more detailed information that can be considered in evolutionary and ecological contexts. We propose that using low‐resolution 3D scans may successfully substitute landmark analyses because it overcomes the subjective landmarking. Analyses of 3D scans with higher resolution than used in this study will provide surface roughness information at the mineralogical level. We suggest that fractal dimension analyses of a combination of differently resolved 3D models will significantly improve the quality of shell morphometrics.

Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning

“…CNNs can complement existing methods for morphological studies such as morphological matrix ( Dai, Korn & Song, 2021 ), landmark ( Bazzi et al, 2018 ), fractal dimensions ( Wiese et al, 2022 ), ornamentation index ( Miao et al, 2022 ), conch properties ( De Baets, 2021 ), and 3D morphological methods ( Klinkenbußet al, 2020 ) and provide new perspectives for studying the morphological evolution of fossils in the future. Geometric morphometry requires the extraction of fossil features by labelling manually and performing descending operations ( e.g.…”