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bioRxiv2023

Universal baleen whale microsatellite panel for individual identification and power to detect parentage

Highly polymorphic single tandem repeat loci (STR, also known as microsatellite loci) remain a familiar, cost efficient class of markers for genetic analyses in ecology, behavior and conservation. We characterize a new universal set of ten STR loci (from 28 potential candidate loci) in seven baleen whale species, which are optimized for PCR amplification in two multiplex reactions along with a Y chromosome marker for sex determination. The optimized, universal set of STR loci provides an ideal starting point for new studies in baleen whales aimed at individual-based and population genetic studies, and facilitates data sharing among research groups. Data from the new STR loci were combined with genotypes from other published STR loci to assess the power to assign parentage (paternity) using exclusion in four species: fin whales, humpback whales, blue whales and bowhead whales. We argue that parentage studies should present a power analysis to demonstrate that the specific data are sufficiently informative to assign parentage with statistical rigor.

genetics↗

bioRxiv2023

Deep audio embeddings for vocalisation clustering

The study of non-human animals communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a species. When conducted by human experts, the formal description of vocal repertoires can be laborious and/or biased. This motivates computerised assistance for this procedure, for which machine learning algorithms represent a good opportunity. Unsupervised clustering algorithms are suited for grouping close points together, provided a relevant representation. This paper therefore studies a new method for encoding vocalisations, allowing for automatic clustering to alleviate vocal repertoire characterisation. Borrowing from deep representation learning, we use a convolutional auto-encoder network to learn an abstract representation of vocalisations. We report on the quality of the learnt representation, as well as of state of the art methods, by quantifying their agreement with expert labelled vocalisation types from 7 datasets of other studies across 6 species (birds and marine mammals). With this benchmark, we demonstrate that using auto-encoders improves the relevance of vocalisation representation which serves repertoire characterisation using a very limited number of settings. We also publish a Python package for the bioacoustic community to train their own vocalisation auto-encoders or use a pretrained encoder to browse vocal repertoires and ease unit wise annotation.

bioinformatics↗