de Baets K (2021)
Publication Language: English
Publication Type: Other publication type
Publication year: 2021
Original Authors: Floe Foxon
Series: Peer Community in Palaeontology
Article Number: 100010
URI: https://paleo.peercommunityin.org/articles/rec?id=80
DOI: 10.24072/pci.paleo.100010
Open Access Link: http://dx.doi.org/10.24072/pci.paleo.100010
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.
APA:
de Baets, K. (2021). Performance of machine-learning approaches in identifying ammonoid species based on conch properties.
MLA:
de Baets, Kenneth. Performance of machine-learning approaches in identifying ammonoid species based on conch properties. 2021.
BibTeX: Download