Using Approximate Bayesian Computation to infer sex ratios from acoustic data.

ABSTRACT: Population sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex. Acoustic sexing is an option if vocalizations differ between sexes, but is precluded by overlapping distributions of the values of male and female vocalizations in many species. A method allowing the inference of sex ratios despite such an overlap will therefore greatly increase the information extractable from acoustic data. To meet this demand, we developed a novel approach using Approximate Bayesian Computation (ABC) to infer the sex ratio of populations from acoustic data. Additionally, parameters characterizing the male and female distribution of acoustic values (mean and standard deviation) are inferred. This information is then u

SUBMITTER: Lehnen L 

PROVIDER: S-EPMC6013104 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature