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Evolution of facial color pattern complexity in lemurs.

ABSTRACT: Interspecific variation in facial color patterns across New and Old World primates has been linked to species recognition and group size. Because group size has opposite effects on interspecific variation in facial color patterns in these two radiations, a study of the third large primate radiation may shed light on convergences and divergences in this context. We therefore compiled published social and ecological data and analyzed facial photographs of 65 lemur species to categorize variation in hair length, hair and skin coloration as well as color brightness. Phylogenetically controlled analyses revealed that group size and the number of sympatric species did not influence the evolution of facial color complexity in lemurs. Climatic factors, however, influenced facial color complexity, pigmentation and hair length in a few facial regions. Hair length in two facial regions was also correlated with group size and may facilitate individual recognition. Since phylogenetic signals were moderate to high for most models, genetic drift may have also played a role in the evolution of facial color patterns of lemurs. In conclusion, social factors seem to have played only a subordinate role in the evolution of facial color complexity in lemurs, and, more generally, group size appears to have no systematic functional effect on facial color complexity across all primates.

SUBMITTER: Rakotonirina H

PROVIDER: S-EPMC5680244 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Evolution of facial color pattern complexity in lemurs.

Rakotonirina Hanitriniaina H Kappeler Peter M PM Fichtel Claudia C

Scientific reports 20171109 1

Interspecific variation in facial color patterns across New and Old World primates has been linked to species recognition and group size. Because group size has opposite effects on interspecific variation in facial color patterns in these two radiations, a study of the third large primate radiation may shed light on convergences and divergences in this context. We therefore compiled published social and ecological data and analyzed facial photographs of 65 lemur species to categorize variation i ...[more]

PMID: 29123214

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Project description:Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition.CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec.NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy.This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.

Dataset Information

Evolution of facial color pattern complexity in lemurs.

Publications

Evolution of facial color pattern complexity in lemurs.

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