# The Acoustic Index User's Guide: a practical manual for defining, generating and understanding current and future acoustic indices

Tom Bradfer-Lawrence, Brad Duthie, Carlos Abrahams, Matyáš Adam, Ross

Barnett, Amy Beeston, Jennifer Darby, Benedict Dell, Nick Gardner, Amandine Gasc, et al.

► To cite this version:

Tom Bradfer-Lawrence, Brad Duthie, Carlos Abrahams, Matyáš Adam, Ross Barnett, et al. The Acoustic Index User's Guide: a practical manual for defining, generating and understanding current and future acoustic indices. Methods in Ecology and Evolution, 2024, 16 (6), pp.1040-1050. 10.1111/2041-210X.14357. hal-04689523

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# The Acoustic Index User's Guide: A practical manual for defining, generating and understanding current and future acoustic indices

Tom Bradfer-Lawrence¹,² ID | Brad Duthie² ID | Carlos Abrahams³,⁴ ID | Matyáš Adam⁵ ID |
Ross J. Barnett² ID | Amy Beeston⁶ ID | Jennifer Darby⁷ ID | Benedict Dell⁸ ID |
Nick Gardner⁹ ID | Amandine Gasc¹⁰ ID | Becky Heath¹¹,¹² ID | Nia Howells⁷ |
Magnus Janson¹³ ID | Maria-Viktoria Kyoseva² ID | Thomas Luypaert¹⁴ ID |
Oliver C. Metcalf⁷,¹⁵ ID | Anna E. Nousek-McGregor¹⁶ ID | Frederica Poznansky¹⁷ |
Samuel R. P.-J. Ross¹⁸ ID | Sarab Sethi¹⁹ ID | Siobhan Smyth⁷ ID | Emily Waddell² ID |
Jérémy S. P. Froidevaux²,²⁰ ID

**Correspondence**

Tom Bradfer-Lawrence
Email: tom.bradferlawrence@rspb.org.uk

**Funding information**

Engineering and Physical Sciences
Research Council, Grant/Award Number:
EP/V007866/1; Leverhulme Trust, Grant/
Award Number: RPG-2020-160 and ECF-
2020-571

Handling Editor: Aaron Ellison

**Abstract**

1. Ecoacoustics, the study of environmental sound, is a rapidly growing discipline offering ecological insights at scales ranging from individual organisms to whole ecosystems. Substantial methodological developments over the last 15 years have streamlined extraction of ecological information from audio recordings. One widely used set of methods are acoustic indices, which offer numerical summaries of the spectral, temporal and amplitude patterns in audio recordings.

2. Currently, the specifics of each index's background, methodology and the sound-scape patterns they are designed to summarise, are spread across multiple sources. Critically, details of index calculation are sometimes scarce, making it challenging for users to understand how index values are generated. Discrepancies in understanding can lead to misuse of acoustic indices or reporting of spurious results. This hinders ecological inference, replicability and discourages adoption of these tools for conservation and ecosystem monitoring, where they might otherwise provide useful insight.

3. Here we present the Acoustic Index User's Guide—an interactive RShiny web app that defines and deconstructs eight of the most commonly used acoustic indices to facilitate consistent application across the discipline. We break the acoustic indices calculations down into easy-to-follow steps to better enable practical application and critical interpretation of acoustic indices. We demonstrate typical soundscape patterns using a suite of 91 example audio recordings: 66 real-world

<figure><img src="image_3.png" /></figure>
soundscapes from terrestrial, aquatic and subterranean systems around the
world, and 25 synthetic files demonstrating archetypal soundscape patterns. Our
interpretation figures signpost specific soundscape patterns likely to be reflected
in acoustic indices' values.

4. This RShiny app is a living resource; additional acoustic indices will be added in the future through collaboration with authors of pre-existing and new indices. The app also serves as a best-practice template for the information required when publishing new acoustic indices, so that authors can facilitate the widest possible understanding and uptake of their indices. In turn, improved understanding of acoustic indices will aid effective hypothesis generation, application and interpretation in ecological research, ecosystem monitoring and conservation management.

KEYWORDS

autonomous recording unit, bioacoustics, ecoacoustics, passive acoustic monitoring, soundscape

1 | INTRODUCTION

Ecoacoustics, the study of environmental sound, offers ecological
insights at scales ranging from individual organisms to whole ecosys-
tems (Sueur & Farina, 2015). A foundational concept in ecoacoustics
is the soundscape, which refers to all sound at a location, comprised
of biological (biophony, e.g. vocalising animals), geophysical (geoph-
ony, e.g. the wind in vegetation) and anthropogenic sounds (anthro-
pophony, sounds of human origin such as engine noise) (Pijanowski
et al., 2011). In general, soundscape recordings are enormously
complex and temporally variable and require summarising prior to
ecological analysis.

There are two broad approaches to summarising audio re-
cordings: sound event detection and whole soundscape analyses.
Extracting and identifying individual calls or other sound events pro-
vides valuable ecological data (e.g. McGinn et al., 2023; Rhinehart
et al., 2020; Wood et al., 2021), but this approach can be time-
consuming, often requires specialist technical expertise, and as-
sumes species' vocalisations are already known (Bradfer-Lawrence
et al., 2023). Alternatively, a whole soundscape approach focuses on
broader acoustic dynamics and patterns, offering insights into eco-
system functioning, integrity and complexity. Soundscape studies
can provide novel perspectives in a broad range of ecological ques-
tions (Ross et al., 2023), for example this approach has been used to
assess habitat quality, and the impacts of, and recovery from, distur-
bance (e.g. Barbaro et al., 2022; Burivalova et al., 2022; Gottesman
et al., 2021).

Soundscape analyses often utilise acoustic indices—quantita-
tive metrics reflecting a range of spectral, temporal and amplitude
patterns in the soundscape (Sueur, 2018). These values can then
be analysed in a range of ways: offering qualitative descriptions
of soundscape patterns (Towsey et al., 2014); acting as proxies for
biodiversity metrics such as species richness (Eldridge et al., 2018;

Sethi et al., 2023); and providing input for machine learning mod-
els (Bradfer-Lawrence et al., 2019; Do Nascimento et al., 2020).
Machine learning approaches have also been used directly in similar
contexts, with analysis of the model's learned features rather than
the acoustic indices we discuss here (Sethi et al., 2020). However,
while machine learning analyses can capture broad soundscape pat-
terns, ecological insight is currently limited as learned features are
generally not interpretable (although this situation will likely change
with ongoing developments, see Gibb et al., 2024).

Acoustic index values can be readily generated in a range
of programming environments, including R ('seewave', Sueur,
Aubin, & Simonis, 2008; 'soundecology', Villanueva-Rivera &
Pijanowski, 2018) and Python ('scikit-maad', Ulloa et al., 2021),
in standalone software such as AnalysisPrograms.exe (Towsey
et al., 2018) and Kaleidoscope Pro (Wildlife Acoustics), and in on-
line platforms such as Arbimon (Rainforest Connection). While index
values are straightforward to compute with such software, their
interpretation can be challenging. Similar soundscape patterns can
arise from very different sound sources, potentially confounding
inference (Bradfer-Lawrence et al., 2023). This is particularly the
case when using acoustic indices as proxies for biodiversity metrics,
and contradictory patterns have been reported in the literature re-
garding the hypothesised link between acoustic diversity and bio-
logical diversity (Alcocer et al., 2022). This may stem from genuine
differences among soundscapes from different ecosystems (e.g.
Barbaro et al., 2022; Sethi et al., 2023), or from methodological de-
cisions made during data collection and analysis (Bradfer-Lawrence
et al., 2020; Gasc et al., 2015). Most soundscape analyses focus on
human-audible frequencies (i.e., 20-20,000 Hz), and additional fore-
thought will be required when calculating and interpreting acoustic
indices from ultrasonic recordings (Silva et al., 2022) or those dom-
inated by low-frequency sounds such as soil soundscapes (Metcalf
et al., 2024).
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Example tab from the Acoustic Index User's Guide app, showing details for the Acoustic Complexity Index covering general information about the index, the example recordings and the index calculation steps. Numbers in circles refer to the points in the main text.</figcaption></figure>
## 9 Soundscape examples

### Sound source

<figure><img src="image_5.png" /></figure>

Play

Stop

### ACI value

1856.35

### Spectrogram

<figure><img src="image_6.png" /></figure>

**Metadata** Predawn chorus in tropical forest, with high levels of acoustic activity from frogs, insects and nightjars. Amazon Research Centre, Amazonia Expeditions, Peru. Recorded at 05:23am on 23rd May 2022 with an AudioMoth recorder at 32 kHz by Nick Gardner.

## 10 Calculation

### Step 1 - Generate spectrogram

<figure><img src="image_7.png" /></figure>

sound file

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

spectrogram

### Step 2 - Extract dB values for each spectrogram cell

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

### Step 3: For each 5-second cluster in each frequency band:

5 Seconds

<figure><img src="image_15.png" /></figure>

**3.1:** Sum total amplitude value in all time steps
$$12 + 14 + 18 + 24 + 28 = 96$$

**3.2:** Sum total differences in amplitude among time steps
$$2 + 4 + 6 + 4 = 16$$

**3.3:** Calculate 'difference in amplitude' as proportion of 'total amplitude'
$$16 / 96 = 0.17$$

### Step 4: Sum all 5-second proportion values (step 3.3) within each frequency band

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

### Step 5: Sum across all frequency bands to get ACI

$$0.08 + 0.09 + 1.60 + 2.51 + 2.17 + 2.17 + 1.94 + 1.27 = \mathbf{11.89}$$

<figure><img src="image_18.png" /><figcaption>FIGURE 1 (Continued)</figcaption></figure>
<figure><img src="image_19.png" /></figure>

**FIGURE 2** Effect of Fourier transform window length on the spectro-temporal resolution of spectrograms derived from recordings with different sampling rates, and the consequent effects on acoustic index values. (a) There is a direct trade-off between the number of frequency bands and the number of temporal frames. The number of frequency bands is half the window length, but the width of each band in Hz will vary depending on the sampling rate of the recording; larger window lengths improve frequency resolution (i.e., result in narrower frequency bands). The number of temporal frames is determined by the window length divided by the sampling rate; larger window lengths degrade temporal resolution (i.e., give fewer temporal frames). (b) Three example 10-s spectrograms from the same audio recording showing the effect of increasing window length on spectrogram resolution between 0 and 10 kHz, increasing frequency resolution at the expense of temporal resolution (window lengths from left to right: 128, 512, 2048). (c) For some indices this can have strong effects on acoustic index values. This might result in consistently higher values as in ACI (left), or incremental divergence as values increase such as Bio (right), further examples showing different patterns in the other indices are in Figure S1.

Critically, whether using acoustic indices for whole soundscape analyses or as biodiversity proxies, accurate interpretation requires a thorough background understanding of how soundscape patterns will be reflected in index values (Bradfer-Lawrence et al., 2023). However, until now it has been challenging to acquire the knowledge required for interpretation. Information about acoustic indices is spread across multiple, disparate publications (e.g. see Sueur, 2018; Sueur, Pavoine, et al., 2008; Villanueva-Rivera & Pijanowski, 2018) and, in some cases, details regarding their underlying calculations are limited. Extracting precise calculation details including undocumented nuances has hitherto required deconstructing the computational source code, representing a substantial barrier to wider use, comprehension and correct interpretation. Many indices have default parameters that could be altered to better suit specific use cases, but there are few resources exploring the effects of such changes (Metcalf et al., 2021, 2023). Uncertainty around the

interpretation of acoustic indices limits their effective use in surveys and monitoring, discourages new users and prevents the wider acceptance of ecoacoustics as an evidence tool that could guide policy formulation, conservation and land management.

## 2 | THE ACOUSTIC INDEX USER'S GUIDE

To facilitate greater understanding and uptake of acoustic indices, here we present The Acoustic Index User's Guide. This interactive RShiny web app helps users explore and understand eight of the most frequently used acoustic indices (Alcocer et al., 2022; Minello et al., 2021), which offer complementary categorisations of acoustic patterns (Bradfer-Lawrence et al., 2019). The web app can be accessed at https://ecohack.shinyapps.io/Acoustic_Index_Users_Guide/. For those who prefer to run the app locally, the current
**FIGURE 3** Understanding how acoustic indices reflect soundscape patterns is key to index selection and interpretation, shown here for two example indices. (a) shows Acoustic Complexity Index values from 1-min recordings, (b) shows 10-s sections of corresponding spectrograms for six synthesised (1–6) and two real-world (7 and 8) soundscapes. Index values increase with greater amplitude and temporal variability, but uniformly noisy soundscapes (6) have values as low as quiet soundscapes (1). (c) shows Bioacoustic Index values from 1-min recordings, (d) shows 10-s sections of corresponding spectrograms for five synthesised (1–5) and two real-world (6 and 7) soundscapes; white dashed lines indicate the frequency bounds for this index (default of 2–8kHz). The Bioacoustic Index quantifies amplitude relative to the quietest frequency band, so that the index value increases up to soundscape 4, but then drops again in 5 because the soundscape is uniformly loud within the frequency bounds.

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

version can be downloaded from https://doi.org/10.5281/zenodo.
11041274. Static pages of the current content are in the Supporting
Information.

Collecting and pre-processing audio recordings are important
steps in any ecoacoustics study, but we do not consider these here
as there are many existing resources offering advice on study design
(Metcalf et al., 2023; Sugai et al., 2020). In the Resources tab of the
app, we provide links to some of these resources relating to moni-
toring protocols, equipment selection, methodology and analyses,
including details of the programmes available for calculating acous-
tic indices. For those new to ecoacoustics, we also include a Glossary
of commonly used terms.

Here, we highlight the key features of the Acoustic Index User's
Guide that will assist users' selection and interpretation of acoustic
indices. As noted above, there are a range of options for generat-
ing acoustic indices values, using either script-based or standalone
programmes. These are largely straightforward to use, but under-
standing and interpreting the resulting values requires specialist
knowledge, which is where the Acoustic Index User's Guide can
help. Improved understanding will aid effective hypothesis gener-
ation, application and interpretation—helping users fulfil the critical
goal of explaining the links between acoustic indices and sound-
scape patterns (Bradfer-Lawrence et al., 2023; Ross et al., 2021). We
envisage the app as a useful tool for a wide range of existing and
<figure><img src="image_24.png" /><figcaption>FIGURE 4 Map of the 23 locations where example real-world recordings were collected between 2017 and 2023. In some cases, more than one recording was collected at an individual site, giving a total of 66 1-min recordings.</figcaption></figure>

aspirational acousticians, including researchers, students, land managers and ecological practitioners.

## 2.1 | Understanding acoustic indices

The indices included in the app so far are: Acoustic Complexity Index (ACI; Pieretti et al., 2011); Acoustic Diversity Index (ADI) and Acoustic Evenness Index (Aeve; Villanueva-Rivera et al., 2011); Bioacoustic Index (Bio; Boelman et al., 2007); Acoustic Entropy (H) and its spectral (Hf) and temporal (Ht) components (Sueur, Pavoine, et al., 2008); and Normalised Difference Soundscape Index (NDSI; Kasten et al., 2012).

Each of the acoustic indices has a single dedicated tab, with a consistent structure to facilitate end-user understanding of the soundscape patterns they reflect and their underlying calculations, as shown in the example tab for ACI that contains following sections (numbers correspond to circles in Figure 1):

1. *Index name and abbreviation.*

2. *Original reference* that introduced the index.

3. *Index description* outlining the rationale underlying the index.

4. *Intended patterns* offering a general description of the soundscape features the index is intended to reflect, based on details in the original reference.

5. *Reported patterns* in the wider literature, as these sometimes diverge from those in the original reference, either due to system-specific differences, or methodological choices (such as altering index parameters).

6. *Default settings* including the frequency ranges and Fourier Transform window lengths used in a range of script-based and standalone programmes.

7. *Sensitivity to Fourier Transform window length*, indicating the likely effects of increasing the window length on index values (Box 1).

8. Numeric properties of the *Output values*, such as the bounds, range and distribution.

9. *Example sound recordings* selected using a dropdown menu, from our collection of 91 1-min recordings (see below). A spectrogram for the first 20s of the chosen recording is displayed, and users can opt to play the audio recording. The index value is displayed (generated with a Fourier Transform window of 512), along with a description of the broad soundscape patterns present in the recording and recording metadata.
<figure><img src="image_25.png" /></figure>

10. *Index calculations* broken down into a series of steps with illustrative figures. These are based on calculations in the R packages 'seewave' (Sueur, Aubin, & Simonis, 2008) and 'soundecology' (Villanueva-Rivera et al., 2011), as these were either the original implementations of the indices, or the functions were constructed with input from the index creators. It is important to note that some packages and platforms may have implemented these indices in different ways, meaning they have different calculation steps or do not return identical values to the methods presented here (Villanueva-Rivera, 2015).

## 2.2 | Validating and interpreting acoustic indices

Two crucial steps in any acoustic indices study are validation and interpretation. Users need to statistically test assumed links between indices patterns and response variables, explain what the index values imply about soundscape patterns and, ideally, their ecological relevance (Bradfer-Lawrence et al., 2023). Doing so will require sound-truthing recordings to determine the likely sources of soundscape features. To aid users in identifying soundscape features that may be driving acoustic indices values, we demonstrate patterns using synthesised soundscapes in the Which indices? tab of the app (Figure 3; Figures S2–S6).

Some key points to bear in mind when interpreting index values:

1. In general, any additional sound is likely to increase acoustic diversity, and in turn this will influence acoustic indices values. However, this 'diversity' could be from a new source (e.g. the beginning of a rainstorm), or an existing source making a different sound (e.g. an individual bird varying its call). There are also occasions when certain broadband sounds, such as heavy rain, can mask other sounds leading to a reduction in acoustic diversity (Figure 3). Hence, users need to consider the ecological relevance of any soundscape change.

2. Different sound types will affect indices differently, even if the total amount of acoustic energy is the same. For instance, contrast a bird chorus that has rapid variation among frequencies and over time, with a continuous insect chorus which covers a broad frequency range and varies little over time (Figure 3).

3. One individual index value on its own is rarely interpretable and ecological relevance can only be derived from a series of values from multiple acoustic samples. For example, the temporal variability in index values among sites can provide important insights into habitat quality and ecosystem response to perturbation (Bradfer-Lawrence et al., 2019; Burivalova et al., 2019).

4. Precise indices values will depend on the recording protocol (Metcalf et al., 2023), use of compression (Heath et al., 2021), calculation software (Villanueva-Rivera, 2015) and index parameters, such as frequency bounds (Metcalf et al., 2021). Of particular importance is the link between the sample rate of the recordings and the Fourier Transform window length, as this determines

the resolution of the spectrogram which forms the basis of most acoustic indices calculations (Box 1).

5. If different types of sound create similar spectrogram patterns, they will result in similar index values (Bradfer-Lawrence et al., 2023). Identifying and resolving such confounds relies on sound-truthing recordings and contrasting multiple indices.

# 3 | ADDITIONAL CONTENT

There are two further tabs in the app:

(i) *Example recordings.* We have assembled a set of 91 1-min recordings (Bradfer-Lawrence, Abrahams, et al., 2024). 66 were collected in air, water and soil, from sites around the world (Figure 4). They include a wide range of soundscape patterns, reflecting diverse sources of sound including biophony, geophony and anthropophony. The recordings were collected with a range of equipment and sample rates; full details are available in the associated metadata file. We also include 25 synthetic soundscapes created with Audacity software. Collectively, these offer users the opportunity to explore a variety of soundscapes and cultivate their understanding of the links between acoustic

## BOX 1 Effect of spectrogram dimensions on acoustic indices values

Digital audio recordings in .wav format are a single long vector of values. For most acoustic indices, this vector needs to be converted to a spectrogram using a Fourier Transform prior to index calculation (Sueur, 2018). A spectrogram is a matrix of amplitude values providing a visual representation of the sound signal, with time on the x-axis and frequency on the y-axis (Figure 2). Given a recording of a set duration, the number of cells in a spectrogram is constant, but the size of the Fourier transform window determines the trade-off between temporal and frequency resolution (Figure 2).

Until now, the implications of spectrogram resolution on acoustic indices have been largely overlooked, despite their potentially critical influence on the values returned. For example, where an index is cumulative, increasing the number of frequency bands will—on average—lead to higher index values regardless of the soundscape patterns (Figure 2; Figure S1). Although this feature should not affect comparisons within recording sets (assuming common recording and calculation settings), it does hinder comparisons among studies. In the dedicated index tabs (Section 2.1), we highlight which indices are sensitive to spectrogram dimensions, indicating the effects of altering the Fourier Transform window.
patterns and indices values. The recordings are free to use under a CC-BY licence and can be downloaded along with the metadata from https://doi.org/10.5281/zenodo.11004284.

(ii) *About this guide.* Introducing the project's origins, the app's creators and suggested citation.

## 4 | FUTURE DEVELOPMENTS AND CONCLUSION

The key motivation for creating the Acoustic Index User's Guide as an Rshiny app was to ensure that it remains an up-to-date resource, growing with the ongoing expansion of ecoacoustics and incorporating specific development of new acoustic indices or best practices for existing indices as they become available. In line with the ethos of open science, we welcome input from other users who would be interested in helping to create additional pages, either for existing indices we have not yet covered or new ones when they are developed. We encourage interested individuals to contact the authors.

Validation and interpretation are key steps in acoustic indices research that are sometimes neglected (Alcocer et al., 2022; Bradfer-Lawrence et al., 2023). The Acoustic Index User's Guide should help remedy that by providing current, easy-to-follow guidance in using acoustic indices and so offer the knowledge base for researchers to deliver informed interpretation of soundscape patterns. We hope that all researchers can use this guide to accurately link ecoacoustics with a wide range of key ecological processes for research in conservation and environmental management.

### AUTHOR CONTRIBUTIONS

Tom Bradfer-Lawrence devised the initial concept and led the writing of the manuscript. Brad Duthie led the coding of the app. Jérémy S. P. Froidevaux secured the funding. All authors developed and expanded the original idea, generated content for the app, commented on manuscript drafts and gave final approval for publication.

### AFFILIATIONS

¹Centre for Conservation Science, RSPB, Edinburgh, UK; ²Biological & Environmental Sciences, University of Stirling, Stirling, UK; ³Baker Consultants Ltd, Matlock, Derbyshire, UK; ⁴Nottingham Trent University, Nottingham, UK; ⁵Department of Environmental Security, Faculty of Logistics and Crisis Management, Tomas Bata University in Zlín, Zlín, Czechia; ⁶Art and Design, University of the Highlands and Islands, Inverness, UK; ⁷Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK; ⁸Institute of Sound and Vibration Research, University of Southampton, Southampton, UK; ⁹Florida Museum of Natural History, University of Florida, Gainesville, Florida, USA; ¹⁰Aix Marseille University, University of Avignon, CNRS, IRD, IMBE, Aix-en-Provence, France; ¹¹Department of Zoology, University of Cambridge, Cambridge, UK; ¹²King's College, University of Cambridge, Cambridge, UK; ¹³School of Applied Sciences/Centre for Conservation & Restoration Science, Edinburgh Napier University, Edinburgh, UK; ¹⁴Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway; ¹⁵Lancaster Environment Center, Lancaster University, Lancaster, UK; ¹⁶School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow,

Glasgow, UK; ¹⁷Department of Earth and Environmental Science, University of Exeter, Exeter, UK; ¹⁸Integrative Community Ecology Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan; ¹⁹Department of Life Sciences, Imperial College London, London, UK and ²⁰CESCO, Museum National d'Histoire Naturelle, CNRS, Sorbonne University, Paris, France

### ACKNOWLEDGEMENTS

This project began at the EcoHacK 2022 workshop at the University of Stirling. The workshop and the RShiny app hosting fees were funded by the UK Acoustics Network (UKAN+) via EPSRC grant number EP/V007866/1. Tom Bradfer-Lawrence was supported by a Leverhulme Trust research grant (number RPG-2020-160). Jérémy Froidevaux was supported by a Leverhulme Trust early-career fellowship (award ECF-2020-571). Sam Ross thanks the OIST Environmental Sciences and Informatics Section for hosting the OKEON acoustic data. We thank two anonymous reviewers for suggestions that improved earlier versions of the app and paper.

### CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

### DATA AVAILABILITY STATEMENT

The current version of the app is available from https://ecohack.shinyapps.io/Acoustic_Index_Users_Guide/. A baseline version of the app which can be run locally is available from https://zenodo.org/records/11041274 (Bradfer-Lawrence, Duthie, et al., 2024). The example recordings are available in .wav format along with metadata from https://zenodo.org/records/11004284 (Bradfer-Lawrence, Abrahams, et al., 2024).

### ORCID

Tom Bradfer-Lawrence https://orcid.org/0000-0001-6045-4360
Brad Duthie https://orcid.org/0000-0001-8343-4995
Carlos Abrahams https://orcid.org/0000-0003-0301-5585
Matyáš Adam https://orcid.org/0000-0003-3602-4955
Ross J. Barnett https://orcid.org/0009-0000-1392-8404
Amy Beeston https://orcid.org/0000-0003-2796-1947
Jennifer Darby https://orcid.org/0009-0000-1812-5328
Benedict Dell https://orcid.org/0000-0002-2987-222X
Nick Gardner https://orcid.org/0000-0002-7268-4179
Amandine Gasc https://orcid.org/0000-0001-8369-4930
Becky Heath https://orcid.org/0000-0002-9111-6994
Magnus Janson https://orcid.org/0009-0002-7843-3837
Maria-Viktoria Kyoseva https://orcid.org/0009-0007-1473-8742
Thomas Luypaert https://orcid.org/0000-0001-7491-7418
Oliver C. Metcalf https://orcid.org/0000-0003-3441-2591
Anna E. Nousek-McGregor https://orcid.org/0000-0001-8910-6490
Samuel R. P.-J. Ross https://orcid.org/0000-0001-9402-9119
Sarab Sethi https://orcid.org/0000-0002-5939-0432
Siobhan Smyth https://orcid.org/0009-0001-9462-5487
Emily Waddell https://orcid.org/0000-0003-2555-6390
Jérémy S. P. Froidevaux https://orcid.org/0000-0001-6850-4879
<figure><img src="image_26.png" /></figure>

REFERENCES

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Barbaro, L., Sourdril, A., Froidevaux, J. S. P., Cauchoux, M., Calatayud, F., Deconchat, M., & Gasc, A. (2022). Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes. *Landscape Ecology*, **37**(4), 1125-1143. https://doi.org/10.1007/s10980-021-01391-8

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Figure S1:** Effect of Fourier Transform (FFT) window length on acoustic index values.

**Figure S2:** Acoustic Diversity Index values from 60-s recordings, and 10-s sections of corresponding spectrograms for five synthesised (1–5) and two real-world (6 and 7) soundscapes.

**Figure S3:** Acoustic Evenness Index values from 60-s recordings, and 10-s sections of corresponding spectrograms for five synthesised (1–5) and two real-world (6 and 7) soundscapes.

**Figure S4:** Spectral Entropy Index values from 60-s recordings, and 10-s sections of corresponding spectrograms for five synthesised (1–5) and two real-world (6 and 7) soundscapes.

**Figure S5:** Temporal Entropy Index values from 60-s recordings, and 10-s sections of corresponding spectrograms for five synthesised (1–5) and two real-world (6 and 7) soundscapes.

**Figure S6:** Normalised Difference Soundscape Index values from 60-s recordings, and 10-s sections of corresponding spectrograms for five synthesised (1–7) and two real-world (8 and 9) soundscapes.

**File S1:** Current content of the Acoustic Index User's Guide app.

**How to cite this article:** Bradfer-Lawrence, T., Duthie, B., Abrahams, C., Adam, M., Barnett, R. J., Beeston, A., Darby, J., Dell, B., Gardner, N., Gasc, A., Heath, B., Howells, N., Janson, M., Kyoseva, M.-V., Luypaert, T., Metcalf, O. C., Nousek-McGregor, A. E., Poznansky, F., Ross, S.-J., ... Froidevaux, J. S. P. (2025). The Acoustic Index User's Guide: A practical manual for defining, generating and understanding current and future acoustic indices. *Methods in Ecology and Evolution*, 16, 1040–1050. https://doi.org/10.1111/2041-210X.14357