Conceptual and statistical problems with the use of the Shannon-Weiner entropy index in bioacoustic analyses. Issue 4 (4th July 2019)

• Record Type: Journal Article
• Authors: Sandoval, Luis; Barrantes, Gilbert; Wilson, David R.
• Is Part Of: Bioacoustics. Volume 28:Issue 4(2019)
• Page Start: 297
• Page End: 311
• Publication Date: 2019-07-04
• Subjects: Ecoacoustics -- bioacoustics -- repertoire -- Shannon-Weiner entropy index -- statistical analysis -- soundscape; Bioacoustics -- Periodicals; Sound production by animals -- Periodicals; Animal sounds -- Periodicals; Sound recordings -- Periodicals; 591.59405
• DOI: 10.1080/09524622.2018.1443286
• Languages: English
• ISSNs: 0952-4622

Abstract

Abstract: Information theory and its indices were developed for human communication to predict the amount of information transferred in a message. One such index, the Shannon-Weiner index (SWI), has often been used to analyse information from other fields in which its application may not be appropriate. In ecoacoustics, SWI is used to compare acoustic diversity (i.e. a measure derived by integrating the richness and abundance of animal sounds) between locations. In animal communication, SWI is used to quantify repertoire complexity (i.e. a measure derived by integrating the number and abundance of sound types produced by individuals or species) as an approach to understanding signal evolution. We discuss problems associated with using the SWI in ecoacoustics and animal communication. Specifically, we discuss conceptual and statistical problems associated with the SWI and then illustrate these problems using hypothetical data. In ecoacoustics, the SWI's assumptions of random variables and independent samples are often violated. In animal communication, the SWI fails to distinguish among repertoires in which the number of sound types and the abundance of each sound type differ. We also show that other methods do capture these differences. We conclude that the SWI does not adequately represent acoustic diversity or repertoire complexity due to the multiple conceptual and statistical issues associated with its use. We recommend other analytical methods to more fully describe these biological systems, including goodness of fit, Morisita similarity index and Markov chain analysis. These methods provide more information for future comparisons and permit researchers to test hypotheses more directly.