Dietary Ecology of Murinae (Muridae, Rodentia): A Geometric Morphometric Approach

Cano, Ana Rosa Gómez; Fernández, Manuel Hérnandez; Álvarez-Sierra, M. Ángeles

doi:10.1371/journal.pone.0079080

Cited by 34 publications

(17 citation statements)

References 45 publications

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“…The mouse tooth is one of the traits extensively studied regarding shape variation. In paleontology, it is a key character for phylogenetic and dietary inferences ( Misonne, 1969 ; Gómez Cano et al, 2013 ). It has been a model for developmental genetics ( Jernvall and Thesleff, 2012 ; Urdy et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Genomic regions controlling shape variation in the first upper molar of the house mouse

Pallares

Ledevin

Pantalacci

et al. 2017

eLife

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Numerous loci of large effect have been shown to underlie phenotypic variation between species. However, loci with subtle effects are presumably more frequently involved in microevolutionary processes but have rarely been discovered. We explore the genetic basis of shape variation in the first upper molar of hybrid mice between Mus musculus musculus and M. m. domesticus. We performed the first genome-wide association study for molar shape and used 3D surface morphometrics to quantify subtle variation between individuals. We show that many loci of small effect underlie phenotypic variation, and identify five genomic regions associated with tooth shape; one region contained the gene microphthalmia-associated transcription factor Mitf that has previously been associated with tooth malformations. Using a panel of five mutant laboratory strains, we show the effect of the Mitf gene on tooth shape. This is the first report of a gene causing subtle but consistent variation in tooth shape resembling variation in nature.

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Section: Introductionmentioning

confidence: 99%

Genomic regions controlling shape variation in the first upper molar of the house mouse

Pallares

Ledevin

Pantalacci

et al. 2017

eLife

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“…This hypothesis needs testing, since the diet of C. expulsus and C. tener has not been published in the literature. A study on the outline of the first molar of extinct murine rodents demonstrated that different diet groups could be distinguished through geometric morphometric analysis (Cano et al 2013). However, in marmots and some Didelphidae it has been demonstrated that diet is weakly correlated with molar shape (Caumul andPolly 2005, Chemisquy et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia)

Boroni¹,

Lobo²,

Romano³

et al. 2017

Zoologia

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ABSTRACT. The taxonomic identification of micromammals might be complicated when the study material is fragmented, as it is the case with pellets and fossil material. On the other hand, tooth morphology generally provides accurate information for species identification. Teeth preserve notably well, retaining their original morphology, unlike skulls and mandibles, which can get crushed or have missing parts. Here, we explored a geometric morphometrics approach (GM) to identify fragmented specimens of two sympatric Calomys Waterhouse, 1837 species -Calomys tener (Winge, 1888) and Calomys expulsus (Lund, 1841) -using the morphology of intact molars as the basis for identification. Furthermore, we included some specimens of uncertain taxonomic identification to test their affinities and the utility of the shape of the molar to identify incomplete specimens. We evaluated the variations in the shape of the first upper molar (M1) among 46 owl pellets specimens of Calomys, including C. expulsus (n = 15), C. tener (n = 15), and unidentified specimens treated as Calomys sp. (n = 16) through GM analysis using 17 landmarks. The data was explored using PCA, PERMANOVA, and Discriminant analyses over the Procrustes residuals matrix were applied to evaluate inter-and intraspecific shape differences. Also, we evaluated whether allometric shape differences could impact the data, but found no evidence of a correlation between size and shape. Our results support that shape differences in the M1 are effective for discriminating between C. tener and C. expulsus. Moreover, the unidentified specimens do not represent a third shape but could be identified with confidence either as C. tener or C. expulsus. Our results show that even with fragmentary materials, GM is a feasible and useful tool for exploring inter-specific shape differences and assisting in taxonomic identification as a complement to traditional qualitative description of diagnostic features in poorly preserved specimens.

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“…In most cases, GMM analyses require the manual acquisition of landmarks or outline data. In the case of the murine teeth, each molar occlusal view was manually delineated for the collection of the points along the outline, following a procedure applicable to different rodent taxa (Gómez Cano et al 2013;Ledevin et al 2010;Renaud et al 1996). The data acquisition for the ~1500 mice presented here therefore required, in total, several weeks of full-time fastidious work of an experienced operator.…”

Section: Deep Learning Algorithms As a Promising Tool For The Identifmentioning

confidence: 99%

Deep learning for species identification of modern and fossil rodent molars

Miele

Dussert

Cucchi

et al. 2020

Preprint

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Reliable identification of species is a key step to assess biodiversity. In fossil and archaeological contexts, genetic identifications remain often difficult or even impossible and morphological criteria are the only window on past biodiversity. Methods of numerical taxonomy based on geometric morphometric provide reliable identifications at the specific and even intraspecific levels, but they remain relatively time consuming and require expertise on the group under study. Here, we explore an alternative based on computer vision and machine learning. The identification of three rodent species based on pictures of their molar tooth row constituted the case study. We focused on the first upper molar in order to transfer the model elaborated on modern, genetically identified specimens to isolated fossil teeth. A pipeline based on deep neural network automatically cropped the first molar from the pictures, and returned a prediction regarding species identification. The deep-learning approach performed equally good as geometric morphometrics and, provided an extensive reference dataset including fossil teeth, it was able to successfully identify teeth from an archaeological deposit that was not included in the training dataset. This is a proof-of-concept that such methods could allow fast and reliable identification of extensive amounts of fossil remains, often left unstudied in archaeological deposits for lack of time and expertise. Deep-learning methods may thus allow new insights on the biodiversity dynamics across the last 10.000 years, including the role of humans in extinction or recent evolution.

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Dietary Ecology of Murinae (Muridae, Rodentia): A Geometric Morphometric Approach

Cited by 34 publications

References 45 publications

Genomic regions controlling shape variation in the first upper molar of the house mouse

Genomic regions controlling shape variation in the first upper molar of the house mouse

Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia)

Deep learning for species identification of modern and fossil rodent molars

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