The cat family Felidae is one of the most successful carnivore lineages today. However, the study of the evolution of acoustic communication between felids remains a challenge due to the lack of fossils, the limited availability of audio recordings because of their largely solitary and secretive behavior, and the underdevelopment of computational models and methods needed to address acoustic evolutionary questions. This study is a first attempt at developing a machine learning-based approach to the classification of felid calls as well as the identification of acoustic features that distinguish felid call types and species from one another. A felid call dataset was developed by extracting audio clips from diverse sources. The audio clips were manually annotated for call type and species. Due to the limited availability of samples, this study focused on the Pantherinae subfamily. Time-frequency features were then extracted from the Pantherinae dataset. Finally, several classification algorithms were applied to the resulting data. We achieved 91% accuracy for this Pantherinae call type classification. For the species classification, we obtained 86% accuracy. We also obtained the most predictive features for each of the classifications performed. These features can inform future research into the evolutionary acoustic analysis of the felid group.

No abstract

When studying the evolutionary relationship between a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, e.g., Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available.In this work, we devise a method for detecting correlated evolution among pairs of discrete characters, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoff’s algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by their (co-) occurrence patterns among their respective ancestral assignments. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to study the correlated evolution among vocalizations in the Felidae family, revealing some interesting results.The parcours tool is freely available at https://github.com/murraypatterson/parcours

No abstract

When studying the evolutionary relationships among a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, e.g., Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available. In this work, we devise a method for detecting correlated evolution among pairs of discrete characters, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoff's algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by the (co-) occurrence patterns between the sets of their respective ancestral assignments. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to study the correlated evolution among vocalizations and morphological characters in the Felidae family, revealing some interesting results.