Typifying tiny teeth: A standardized coding system to quantify morphological variation in fish teeth for comparative ecological, evolutionary, and functional morphological studies

Abstract Teeth play a crucial role in vertebrate ecology as one of the main interfaces between predators and their prey. As the most diverse vertebrates on the planet, and the dominant vertebrate consumers in nearly all aquatic ecosystems, fishes – and their teeth – thus play an important role in evolutionary and ecological dynamics of aquatic systems. Most studies of fish tooth morphology, both extinct and extant, have focused on single or a few closely related taxa, rather than attempting to place those teeth in a wider, cross-taxa morphological framework. Further, these studies use disparate criteria to evaluate the morphological variation observed in the teeth specific to their study system, making comparison of fish teeth between studies and across taxa challenging, limiting efforts to understand large-scale patterns of fish tooth morphology. Thus, a common language and morphological framework is necessary to facilitate broader studies of tooth morphology. Here we present a morphometric character coding system that captures morphological variability, drawing on an extensive database of teeth including over 200,000 microfossils and over 800 extant fishes from 61 orders, sampling a wide diversity of fish teeth and their potential range of morphological diversity. The morphological code has been created to be easily replicable across sample and imaging mode, to facilitate direct comparison across studies and independent research teams. Further, the structure of the morphological code is such that additional character states and characters are easily added as new morphological diversity is observed. Finally, we include an R package, ichthyoliths, which executes tooth morphometric disparity analyses based off of the code described in this manuscript, as well as the denticle morphometric code outlined in in Rubin et al. (2025), and can be flexibly used and updated by any researcher who places their fish teeth onto the morphometric code, facilitating cross-study and cross-disciplinary collaborations. This comprehensive effort represents a significant step forward for standardizing the quantification of fish tooth morphology.

Authors

Institutions

Publication Details

Journal
Integrative Organismal Biology
Published
2026-09-17
DOI
https://doi.org/10.1093/iob/obag054
Primary Topic
dental development and anomalies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Typifying tiny teeth: A standardized coding system to quantify morphological variation in fish teeth for comparative ecological, evolutionary, and functional morphological studies

Leah Rubin, Elizabeth C Sibert, Matt Friedman, Morgan Gall et al.
Integrative Organismal Biology
dental development and anomalies
article

Typifying tiny teeth: A standardized coding system to quantify morphological variation in fish teeth for comparative ecological, evolutionary, and functional morphological studies

Leah Rubin, Elizabeth C Sibert, Matt Friedman, Morgan Gall, Nicholas Wallis Mauro, Monica M Marion
article en

Abstract

Abstract Teeth play a crucial role in vertebrate ecology as one of the main interfaces between predators and their prey. As the most diverse vertebrates on the planet, and the dominant vertebrate consumers in nearly all aquatic ecosystems, fishes – and their teeth – thus play an important role in evolutionary and ecological dynamics of aquatic systems. Most studies of fish tooth morphology, both extinct and extant, have focused on single or a few closely related taxa, rather than attempting to place those teeth in a wider, cross-taxa morphological framework. Further, these studies use disparate criteria to evaluate the morphological variation observed in the teeth specific to their study system, making comparison of fish teeth between studies and across taxa challenging, limiting efforts to understand large-scale patterns of fish tooth morphology. Thus, a common language and morphological framework is necessary to facilitate broader studies of tooth morphology. Here we present a morphometric character coding system that captures morphological variability, drawing on an extensive database of teeth including over 200,000 microfossils and over 800 extant fishes from 61 orders, sampling a wide diversity of fish teeth and their potential range of morphological diversity. The morphological code has been created to be easily replicable across sample and imaging mode, to facilitate direct comparison across studies and independent research teams. Further, the structure of the morphological code is such that additional character states and characters are easily added as new morphological diversity is observed. Finally, we include an R package, ichthyoliths, which executes tooth morphometric disparity analyses based off of the code described in this manuscript, as well as the denticle morphometric code outlined in in Rubin et al. (2025), and can be flexibly used and updated by any researcher who places their fish teeth onto the morphometric code, facilitating cross-study and cross-disciplinary collaborations. This comprehensive effort represents a significant step forward for standardizing the quantification of fish tooth morphology.

Integrative Organismal Biology
SUNY College of Environmental Science and Forestry (US), University of Michigan (US), Indiana University Bloomington (US), Woods Hole Oceanographic Institution (US), University of Birmingham (GB)
Openalex Percentile: Top 19%
dental development and anomalies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.