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      Acoustic indices as proxies for biodiversity: a meta‐analysis

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          Abstract

          <p id="d5485730e356">As biodiversity decreases worldwide, the development of effective techniques to track changes in ecological communities becomes an urgent challenge. Together with other emerging methods in ecology, acoustic indices are increasingly being used as novel tools for rapid biodiversity assessment. These indices are based on mathematical formulae that summarise the acoustic features of audio samples, with the aim of extracting meaningful ecological information from soundscapes. However, the application of this automated method has revealed conflicting results across the literature, with conceptual and empirical controversies regarding its primary assumption: a correlation between acoustic and biological diversity. After more than a decade of research, we still lack a statistically informed synthesis of the power of acoustic indices that elucidates whether they effectively function as proxies for biological diversity. Here, we reviewed studies testing the relationship between diversity metrics (species abundance, species richness, species diversity, abundance of sounds, and diversity of sounds) and the 11 most commonly used acoustic indices. From 34 studies, we extracted 364 effect sizes that quantified the magnitude of the direct link between acoustic and biological estimates and conducted a meta‐analysis. Overall, acoustic indices had a moderate positive relationship with the diversity metrics ( <i>r</i> = 0.33, CI [0.23, 0.43]), and showed an inconsistent performance, with highly variable effect sizes both within and among studies. Over time, studies have been increasingly disregarding the validation of the acoustic estimates and those examining this link have been progressively reporting smaller effect sizes. Some of the studied indices [acoustic entropy index (H), normalised difference soundscape index (NDSI), and acoustic complexity index (ACI)] performed better in retrieving biological information, with abundance of sounds (number of sounds from identified or unidentified species) being the best estimated diversity facet of local communities. We found no effect of the type of monitored environment (terrestrial <i>versus</i> aquatic) and the procedure for extracting biological information (acoustic <i>versus</i> non‐acoustic) on the performance of acoustic indices, suggesting certain potential to generalise their application across research contexts. We also identified common statistical issues and knowledge gaps that remain to be addressed in future research, such as a high rate of pseudoreplication and multiple unexplored combinations of metrics, taxa, and regions. Our findings confirm the limitations of acoustic indices to efficiently quantify alpha biodiversity and highlight that caution is necessary when using them as surrogates of diversity metrics, especially if employed as single predictors. Although these tools are able partially to capture changes in diversity metrics, endorsing to some extent the rationale behind acoustic indices and suggesting them as promising bases for future developments, they are far from being direct proxies for biodiversity. To guide more efficient use and future research, we review their principal theoretical and practical shortcomings, as well as prospects and challenges of acoustic indices in biodiversity assessment. Altogether, we provide the first comprehensive and statistically based overview on the relation between acoustic indices and biodiversity and pave the way for a more standardised and informed application for biodiversity monitoring. </p>

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            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Biological Reviews
                Biological Reviews
                Wiley
                1464-7931
                1469-185X
                August 17 2022
                Affiliations
                [1 ]Terrestrial Ecology Group, Departamento de Ecología Universidad Autónoma de Madrid C/ Darwin, 2, Ciudad Universitaria de Cantoblanco, Facultad de Ciencias, Edificio de Biología 28049 Madrid Spain
                [2 ]Centro de Investigación en Biodiversidad y Cambio Global, Universidad Autónoma de Madrid C/ Darwin 2, Ciudad Universitaria de Cantoblanco 28049 Madrid Spain
                [3 ]Department of Life Sciences, GloCEE Global Change Ecology and Evolution Research Group University of Alcalá Alcalá de Henares 28805 Madrid Spain
                [4 ]K. Lisa Yang Center for Conservation Bioacoustics Cornell Lab of Ornithology, Cornell University 159 Sapsucker Woods Road Ithaca NY 14850 USA
                [5 ]Laboratório de Herpetologia e Comportamento Animal, Departamento de Ecologia Instituto de Ciências Biológicas, Universidade Federal de Goiás Campus Samambaia CEP 74001‐970 Goiânia Goiás Brazil
                Article
                10.1111/brv.12890
                0770dcb6-627a-4cde-b208-1282276c9648
                © 2022

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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