Complexity in animal communication: Estimating the size of N-Gram structures

TL;DR

This paper introduces a novel entropy-based method to estimate the complexity and repertoire size of animal communication systems, providing more accurate insights into frequently used signals across species.

Contribution

It applies conditional entropy techniques to estimate animal communication repertoire sizes, improving accuracy over traditional methods and analyzing multiple species with different N-gram lengths.

Findings

Entropy estimates align with known repertoire sizes for common signals.

The method provides more reliable estimates of frequently used signals.

Simulation shows entropy undercounts rare N-grams but captures core communication complexity.

Abstract

In this paper, new techniques that allow conditional entropy to estimate the combinatorics of symbols are applied to animal communication studies to estimate the communication's repertoire size. By using the conditional entropy estimates at multiple orders, the paper estimates the total repertoire sizes for animal communication across bottlenose dolphins, humpback whales, and several species of birds for N-grams length one to three. In addition to discussing the impact of this method on studies of animal communication complexity, the reliability of these estimates is compared to other methods through simulation. While entropy does undercount the total repertoire size due to rare N-grams, it gives a more accurate picture of the most frequently used repertoire than just repertoire size alone.