Measuring individual identity information in animal signals: Overview and performance of available identity metrics
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Date
2019
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Abstract
Identity signals have been studied for over 50 years but, and somewhat remarkably, there is no consensus as to how to quantify individuality in animal signals. While there is a variety of different metrics to quantify individuality, these methods remain un‐validated and the relationships between them unclear.
We contrasted three univariate and four multivariate identity metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets.
Of the metrics examined, Beecher's information statistic (HS) performed closest to theoretical expectations and requirements for an ideal identity metric. It could be also easily and reliably converted into the commonly used discrimination score (and vice versa). Although Beecher's information statistic is not entirely independent of study sampling, this problem can be considerably lessened by reducing the number of parameters or by increasing the number of individuals in the analysis.
Because it is easily calculated, has superior performance, can be used to quantify identity information in single variable or in a complete signal and because it indicates the number of individuals who can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beecher's information statistic in future studies. Consistent use of Beecher's information statistic could enable meaningful comparisons and integration of results across different studies of individual identity signals.
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Grant NCN 2015/19/P/NZ8/02507