Separating overlapping echolocation: An updated method for estimating the number of echolocating animals in high background noise levels

Much can be learned by investigating the click trains of odontocetes, including estimating the number of vocalizing animals and comparing the acoustic behavior of different individuals. Analyzing such information gathered from groups of echolocating animals in a natural environment is complicated by two main factors: overlapping echolocation produced by multiple animals at the same time, and varying levels of background noise. Starkhammar et al. [(2011a). Biol. Lett. 7(6), 836-839] described an algorithm that measures and compares the frequency spectra of individual clicks to identify groups of clicks produced by different individuals. This study presents an update to this click group separation algorithm that improves performance by comparing multiple click characteristics. There is a focus on reducing error when high background noise levels cause false click detection and recordings are of a limited frequency bandwidth, making the method applicable to a wide range of existing datasets. This method was successfully tested on recordings of free-swimming foraging dolphins with both low and high natural background noise levels. The algorithm can be adjusted via user-set parameters for application to recordings with varying sampling parameters and to species of varying click characteristics, allowing for estimates of the number of echolocating animals in free-swimming groups.