Morphological identification in skull between spotted seal and harbor seal using geometric morphometrics

Abstract

The morphology of the skull contains considerable ecological information about a species, because the skull contains sensory organs that are used to look for food, compete for mates, or to migrate. Spotted seals (Phoca largha) and harbor seals (Phoca vitulina) are similar in body size and pelage color but differ in habitat use and reproductive biology. The current study aims to clarify differences in the shapes of skulls in the spotted and harbor seals using geometric morphometrics and to discuss whether ecological differences can explain morphological differences in skulls. First, we discovered that the age at which the shape of skulls stopped changing was 7 years in both species, using the linear‐threshold model. Using a total of 75 landmarks, 54 individuals (25 spotted seals, 29 harbor seals) that were older than the age at which skulls stopped changing were correctly identified at a rate of 100%. The total of 75 landmarks was narrowed down to eight key landmarks that resulted in an identification accuracy rate of 100% using random forests. Of the eight landmarks, seven were related to feeding apparatus, indicated that the harbor seal had a broader mouth and mandible than the spotted seal. Because of both species were dietary generalists and classified as pierce feeders, we suggested that the different features in the shapes of their skulls were caused not only by differences in their feeding behavior but also other differences related to reproductive behavior.

Skull shape and size changes in different subpopulations of the California Sea Lion (Zalophus californianus) in Mexico

“…The final analysis included skull images from 45 specimens from the PC and 5 from the GC used in dorsal view, 33 from the PC and 18 from the GC used in ventral view, and 40 from the PC and 27 from the GC used in lateral view (Table 1, ). Although only 5 specimens in dorsal view represented the GC, we kept this view in the analysis because it is a widely studied view for skull morphology of pinnipeds (Jones and Goswani 2010a, 2010b; Arai et al 2020), which will allow us to make comparison with other taxa, in addition to providing context to the results and patterns observed from the other views.…”

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

“…The radial basis function (RBF) is selected as the kernel function in this study due to its strong classification approach and its versatility in application without requiring prior knowledge of the dataset 54 . SVM-RBF can be defined as follows: where Random Forest Random forests (RF) is an algorithm for classification developed by Breiman (2001) 36 , 55 that is based on bootstrap aggregating or bagging that combines the predictions of multiple decision trees to make a final prediction. This helps to reduce the variance of the individual trees, therefore reducing the overall expected prediction error of the random forest.…”

“…Arai et al applied RF in the context of morphological identification in skulls, specifically between spotted seals and harbour seals, using geometric morphometrics. The study achieved an identification accuracy rate of 100% using RF by narrowing down to a subset of eight key landmarks out of a total of 75 landmarks 36 . The ensemble nature of RF allows it to capture both linear and non-linear relationships in the data, making it robust and accurate for shape classification tasks.…”