A new index for quantifying the ornamentational complexity of animals with shells

Miao, Luyi; Dai, Xu; Song, Hanchen; Backes, André Ricardo; Song, Haijun

doi:10.1002/ece3.9247

Cited by 5 publications

(3 citation statements)

References 96 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Sun,

Liu,

Huang

et al. 2023

PeerJ

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Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available “bivalve and brachiopod fossil image dataset” (BBFID, containing >16,000 “image-label” data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. We achieved >80% identification accuracy at 22 genera and ∼64% accuracy at 343 species using EfficientNetV2s architecture. The intermediate output of the model was extracted and downscaled to obtain the morphological feature space of fossils using t-distributed stochastic neighbor embedding (t-SNE). We found a distinctive boundary between the morphological feature points of bivalves and brachiopods in fossil morphological feature distribution maps. This study provides a possible method for studying the morphological evolution of fossil clades using computer vision in the future.

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Section: Discussionmentioning

confidence: 99%

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

Sun,

Liu,

Huang

et al. 2023

PeerJ

Self Cite

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“…New technology like 3D measurement undoubtedly can provide more comprehensive data on shell ornamentation (e.g., Miao et al, 2022). However, owing to the limitations of inaccessible specimens and equipment, we adopted the methods of measuring and quantifying shell ornamentation that had successfully been applied to fossil brachiopods (Dietl & Kelley, 2001; Leighton, 1999; Vörös, 2010, 2014; Wu et al, 2019; Zhang & He, 2008; Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…New technology like 3D measurement undoubtedly can provide more comprehensive data on shell ornamentation (e.g., Miao et al, 2022).…”

Section: Shell Ornamentation From Different Latitudes and Water Depthmentioning

confidence: 99%

Variation of shell ornamentation with latitude and water depth—A case study using living brachiopods

Bitner

Shi

2023

Ecology and Evolution

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