# Introducing evascape, a model-based soundscape assembler: impact of background sounds on biodiversity monitoring with ecoacoustic indices

Elie Grinfeder, Christian Lorenzi, Yann Teytaut, Sylvain Haupert, Jérôme Sueur

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Elie Grinfeder, Christian Lorenzi, Yann Teytaut, Sylvain Haupert, Jérôme Sueur. Introducing evascape, a model-based soundscape assembler: impact of background sounds on biodiversity monitoring with ecoacoustic indices. Ecological Indicators, 2025, 178, pp.113882. 10.1016/j.ecolind.2025.113882. hal-05510458

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Original Articles

# Introducing evascape, a model-based soundscape assembler: impact of background sounds on biodiversity monitoring with ecoacoustic indices

Elie Grinfeder<sup>a,b,*</sup>, Christian Lorenzi<sup>a</sup>, Yann Teytaut<sup>b</sup>, Sylvain Haupert<sup>b</sup>, Jérôme Sueur<sup>b</sup>

ARTICLE INFO

Keywords:

Soundscape assemblage

Acoustic biodiversity

Ecological validity

Bird behavior

Noise pollution

Ecoacoustic index

A B S T R A C T

The development of ecoacoustics and soundscape ecology is linked to the development of field recording techniques and the production of large and diverse sound databases representative of various environments. However, an important caveat in this approach lies in the lack of reference data, limiting the possibility of developing acoustic biodiversity indices and assessing their sensitivity to environmental change. To address this issue, previous studies have attempted to generate "engineered soundscapes", that is artificial soundscape recordings which offer some control on, among other aspects, species diversity. Unfortunately, these tools lack complexity and realism, limiting their ecological validity. Here, we introduce Evascape, a Python algorithm informed by available knowledge in soundscape ecology aiming at producing assembled soundscapes with high ecological validity. The algorithm offers the possibility to control biophony (number of species, number of individuals, time organization), geophony (wind and rain with adjustable level), anthropophony (aircraft noise) and sound propagation effects (frequency-dependent attenuation function of the habitat). The algorithm was tested using an acoustic database collected in a protected European cold forest. The generated assembled soundscapes were then used to assess a selection of commonly-used ecoacoustic indices across a large number of controlled scenarios, and to identify the indices that respond most strongly to changes in species richness. Evascape opens the path to other ecoacoustics-related research programs, offering the possibility to design empirical and computational experiments with controlled soundscape parameters.

# 1. Introduction

Monitoring and understanding biodiversity with sound can be traced back to the early work of the first naturalists who pioneered transcription of animal sounds through onomatopoeic descriptions. More recently, ecoacoustics grew up with the rise of autonomous recorders and the emergence of signal processing and artificial intelligence techniques to analyze outdoor recordings (Nieto-Mora et al., 2023). Ecoacoustics mainly aims to assess animal population characteristics, animal community structure, landscape composition, and biodiversity level according to space and time (Sueur and Farina, 2015). As such, ecoacoustics can be considered as a descriptive discipline which infers information on ecological processes through *in-situ* observations. It is true that successful field-based experiments have been conducted in both terrestrial (Gomes et al., 2021; McClure et al., 2013, 2017; Sumitani et al., 2021; Ware et al., 2015; Reed et al., 2022) and aquatic

environments (Gordon et al., 2019; Simpson et al., 2004; Vermeij et al., 2010). However, the possibility to experiment with acoustic populations, communities or soundscapes remains rare as controlling acoustic variables in a natural environment is technically challenging and ethically questionable. In particular, the acoustic niche hypothesis (Krause, 1993), which assumes a multidimensional partitioning of the acoustic space that minimizes acoustic competition between local species, could not be tested *in situ* but only with mathematical models (Masumori et al., 2020).

One specific objective of ecoacoustics related to the acoustic niche hypothesis is to estimate the number of species inhabiting a specific site. In parallel with the development of species identification through pattern recognition or machine learning techniques (Stowell, 2022), it was proposed to assess the level of acoustic diversity embedded in a recording by computing a single index also referred as an ecoacoustic index, that would work as a proxy for species richness (Sueur and Farina

<footnote><sup>a</sup> Laboratoire des Systèmes Perceptifs (LSP), CNRS, Ecole normale supérieure, Université Paris Sciences & Lettres, Paris, France</footnote>

<footnote><sup>b</sup> Centre d'Ecologie et des Sciences de la conservation (CESCO), Muséum national d'Histoire naturelle, Centre national de la Recherche Scientifique, Sorbonne Université, Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: elie.grinfeder@gmail.com (E. Grinfeder).</footnote>
et al., 2014). In parallel, several ecoacoustic indices were developed to assess a more general understanding of soundscape composition (Fuller et al., 2015). Mainly developed with field-based recordings (Pieretti et al., 2011; Sueur et al., 2008), the accuracy of some of these indices was tested with “soundscape assemblages”, which were built by mixing species-specific recordings (Gasc et al., 2015; Zhao et al., 2019). In this context, the concept of soundscape refers to the “proximal soundscape”, that is the complex sound mixture that reaches a receiver, before it is processed by internal mechanisms such as perceptual, cognitive and emotional interpretations (Grinfeder et al., 2022b).

None the less, even if former soundscape assemblage studies were pioneering, they hardly took into account the complexity of what are often presented as the three soundscape components: biophony (the sounds produced by non-human life), geophony (the sounds made by abiotic phenomena) and anthropophony (the sounds produced by humans). As an example, a recent ecoacoustic recording campaign made in a protected European cold forest showed that 75 % of the recorded soundscape contained some level of aircraft noise (Grinfeder et al., 2022a). The defects of those previous soundscape assemblers question the validity and accuracy of the results they led to. The sole focus on biodiversity also diverts ecoacousticians from considering and studying other valuable soundscape components such as geophony. There is therefore a clear need for the generation of bird acoustic communities, or more broadly proximal soundscapes, to allow for experimentation through direct manipulation of certain dependent variables such as the number of individuals per species (species abundance), the number of species (species richness), different types of background conditions, and propagation effects.

For the sake of clarity, we here propose to make the distinction between “recorded soundscapes”, which correspond to the actual data collected on site by audio recorders, and “engineered soundscapes”, which are artificially produced in order to mimic the acoustic properties of recorded soundscapes. The ability to produce such engineered soundscapes enables ecologists to access the sound file’s reference data. In the early 2000s, the rise of computational models introduced simulation as “another kind of experimental system” which were considered as complementary to real-life experiments (Peck, 2004). Drawing from Edridge and Kiefer notion of “Synthetic Acoustic Ecology” (2018), the development of soundscape engineering opens the path to “computational experimental ecoacoustics”, as opposed to “field experimental ecoacoustics” which corresponds to the *in situ* experiments introduced earlier. Apart from soundscape simulation which is often used to test the acoustic niche hypothesis (Kadish et al., 2019; Masumori et al., 2020), soundscape engineering can also, among others and depending on the method, be used to train source separation algorithms (Denton et al., 2022), or to develop, tune or select ecoacoustic indices (Gasc et al., 2015; Sueur et al., 2008; Zhao et al., 2019).

However, previous soundscape engineering attempts have often been too simple in their design, leading to the production of sound files that were quite distinct from actual field recordings (Guastavino et al., 2004). One of the most recent and popular examples of soundscape engineering comes from Morrison et al. (2021). In this study, soundscapes were mainly based on local ecological censuses which provided a list of species to assemble. However, a coherent list of species is not enough to produce engineered soundscapes that are faithful to soundscape recordings. As pointed out by Grinfeder et al. (2022b), the biophony of proximal soundscapes results from acoustic behavioral interactions and complex sound propagation effects that substantially alters acoustic data. Moreover, Morrison et al. (2021) used the web-site Xeno-canto (Xeno-canto Foundation, 2025) as a source for bird recordings. Since Xeno-canto recordings are curated by non-professionals with various monitoring tools and methods, soundscape engineering might be improved by using standardized sound databases which are curated by experts.

To address these issues, we developed a new soundscape engineering workflow for terrestrial areas, named “Evascape” for “Ecologically Valid

Soundscape”, and focused on reproducing bird choruses from a protected European cold forest. Particular attention was paid to source recordings which were all drawn from natural sound databases built by experts.

After developing the theoretical grounds necessary to justify Evascape’s specific workflow, the technical principles of Evascape were introduced. In order to illustrate its potential use in ecoacoustics, Evascape was used to test the ability of ecoacoustic indices to work as proxies for species richness and abundance. A database of assembled soundscapes that was representative of multiple biotic and abiotic variables was generated. This database was then used to test a set of frequently used ecoacoustic indices. Altogether, this new soundscape engineering process should pave the way for computational experimental ecoacoustics and connected disciplines.

## 2. Theory

### 2.1. Ecological validity of engineered soundscapes

The concept of “ecological validity” questions the relevance of laboratory experimental protocols, seeking to maintain “the integrity of the real-life situation in the experimental context” (Schmuckler, 2001). Recently, Tarlao et al. (2022) questioned the ecological validity of engineered soundscapes and assessed a difference between experimental results obtained with soundscape engineering and field experiments. Tarlao et al. (2022) approach was guided by a phenomenological conception of the “perceptual soundscape” (Grinfeder et al., 2022b), with an emphasis on social and psychological biases. We introduce here a different definition of soundscape ecological validity, based on the proximal soundscape, which is more adapted to ecoacoustic and psychoacoustic experiments.

With the aim to describe the ecological validity of any scientific experiment, Holleman et al. (2020) identified an artificiality-naturality gradient. Based on this gradient, we define here three types of ecological validity for soundscape engineering: (1) Type-1 deals with the consideration of ecological processes such as ecosystem characteristics (e.g., coherent acoustic communities, congruent geophony) or individual behavior (e.g., consideration of diel and seasonal singing behavior, inter-song time interval or interactive singing strategies); (2) Type-2 deals with the quality of the sound signal (lack of distortion, perceptual realism); and (3) Type-3 deals with listening conditions (recording duration, stereophony/binaurality, visual virtual reality or onsite listening).

Considering the existing literature, we mainly took into account the first two types of ecological validity, focusing on the production process of engineered soundscapes instead of listening conditions. However, there are many ways to produce engineered soundscapes, raising the question of how to navigate this diversity and which method should be employed in which context.

### 2.2. Typology of engineered soundscapes

In order to choose the method most suited to our goals, we defined a general typology of engineered soundscapes. Two main dimensions of soundscape engineering could be highlighted, which are based on methodology discrepancies. First, the means of production, which can consist in either (1) “monolithic” synthesis techniques, or (2) the “assemblage” of separated sound files. The sound sources that are used in soundscape assemblages can be either synthesized or recorded, giving rise to two trends: synthesis-based (e.g., Fornari, 2016; Masumori et al., 2020) or recording-based (e.g., Zhao et al., 2019; Morrison et al., 2021) engineered soundscapes. Second, the goal of soundscape engineering, which depends on the aims of the experimental protocol: (1) “estimating” an existing recorded soundscape in order to uncover data structure and properties, or (2) “generating” new proximal soundscapes for data augmentation, simulation or artistic purposes (Scurto, 2023; de
Baudouin et al., 2024). These two dimensions of soundscape engineer-
ing, monolithic-assembled (the means) and estimated-generated (the
goal), induce four possible combinations that may be used to choose
among soundscape engineering methods in an informed manner.

Monolithic generation consists of creating new proximal soundscapes via an integral, singular process of sound synthesis. Monolithic generated soundscapes can be produced with generative Artificial Intelligence (AI) models (Kong et al., 2020; Bond-Taylor et al., 2022) as suggested by Duarte (2023), or with texture synthesis (McWalter and McDermott, 2018).

Monolithic estimation consists of reproducing recorded soundscapes via an integral, singular process of sound synthesis. Monolithic estimated soundscapes may be used to understand the critical acoustic features defining a real, recorded soundscape. Monolithic estimation can relate, in practice, to the objective function used to train reconstruction-based AI models such as source separators (Lin and Tsao, 2020) or Variational Auto-Encoders (VAEs) (Kingma and Welling, 2022). VAEs have recently been applied to soundscapes (Gibb et al., 2024) as they are able to learn an *audio latent representation* that can be further exploited for various goals (e.g., embeddings definition, information retrieval, new data inference).

Assembled generation consists of creating new proximal soundscapes by combining several well-defined sound sources. To date, this is the most popular method for ecoacoustic data augmentation and simulation. Assembled generation is used to produce databases of engineered soundscapes which can be based on actual ecological processes (Gasc et al., 2015; Morrison et al., 2021) or an abstract conception of bio-phony, resulting in engineered soundscapes with poor Type-1 ecological validity (Gasc et al., 2015; Nelken et al., 1999; Sueur et al., 2008; Zhao et al., 2019).

Assembled estimation consists of reproducing recorded soundscapes by combining well-defined sound sources. Assembled estimated soundscapes are useful for testing soundscape assemblage models and comparing their outputs to “reference” recorded soundscapes.

In the context of testing ecoacoustic indices, we need a process that
allows for data augmentation, while giving a precise control on exper-
imental parameters. Monolithic generated soundscapes appear prom-
ising but they have not been explored in the context of natural areas, and

usually show less control than assembled generated soundscapes.
However and as seen before, there have been several examples of
assembled generated soundscape processes. Drawing from these previ-
ous attempts, it could be possible to design a model-based soundscape
assembler with high Type-1 ecological validity. Still, should this
assembler be recording-based or synthetic-based? In our opinion,
although synthetic-based soundscape assemblers offer great control on
the assembled soundscape components, previous attempts at bird song
synthesis resulted in poor Type-2 ecological validity, with distinguish-
able distortions or uncanny modulation effects. We therefore chose to
develop a recording-based assembler to achieve better Type-2 ecological
validity.

**3. Material and methods**

3.1. Evaspape general principles

Evascape is an open-access soundscape assembling algorithm which
can be used for both soundscape generation and soundscape estimation.
All the workflow was processed with Python software version 3.10.12.
with the packages Scikit-maad v1.4.3 (Ulloa et al., 2021), Bambird
v0.3.1a1 (Michaud et al., 2023) and Librosa v0.10.2 (McFee et al.,
2015). Evascape is available as an open-source package at https://gith
ub.com/ear-team/evascape/. In its first version, Evascape consists in
combining the outcome of two separate processes: the bird channel and
the background channel (Fig. 1).

The bird channel uses recordings of birds from different species. It
extracts the songs and various statistics from the original recordings and,
based on a behavioral algorithm (see Section 3.3.3), produces an
interactive chorus of bird songs. Each bird in the chorus is randomly
located on a one-dimensional spatial axis, which is located between 10
and 80 m from the simulated receiver (microphone). This distance
translates into the simulation of the bird's song sound propagation. The
Evascape python package already includes alternative behavioral algo-
rithms but new behavioral algorithms can also be embedded. Addi-
tionally, the composition of the bird choruses in the following workflow
were randomly chosen according to biodiversity gradients (species
richness or species abundance), but it is also possible to manually select

<figure><img src="image_4.png" /><figcaption>Fig. 1. Evascape general diagram. Yellow rectangles represent the input data which can be manipulated. Each bird can be from the same or different species. The extraction/calibration stage is only done one time to fasten the process, while the assemblage stage is redone every time in order to generate new unique singing sessions for each assembled soundscapes. ISTI stands for “Inter-Song Time Interval”. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
the species of each individuals present in the chorus.

The background channel uses on-site recordings of various types of background sounds/noises in order to control the acoustic environmental conditions in which the bird chorus is occurring. Evascape current version includes background sounds coming from geophonic, anthropophonic and biophonic sources, but it is possible to add new background sounds to the existing database.

To follow the temporal recording design used in previous experiments (Grinfeder et al., 2022a), the duration of the engineered soundscapes was set to 1 min. Depending on the length of the original recordings used for the bird and background channel, it could also be possible to reduce or extend the duration of the assemblages.

## 3.2. Reproducing recordings of a cold protected forest

Evascape was used to reproduce proximal soundscapes of the Risoux forest, a protected European cold forest located in the East of France (Haut-Jura, France). This coniferous forest is part of a long-term ecoacoustic monitoring program, being continuously recorded since July 2018 (Grinfeder et al., 2022a,b; Folliot et al., 2022; Haupert et al., 2023). The biophony of the Risoux being essentially composed of bird songs, insect sounds were considered as background rather than biodiversity indicators. In another setting, insects or geophony could be considered as the “target/signal” channel, while birds could be considered as background.

## 3.3. Bird channel

### 3.3.1. Sound sources

Eight bird species were selected from the Risoux forest's most prevalent species as determined by the inventory of Joveniaux and Chevillard (2014): *Erithacus rubecula* (European Robin), *Fringilla coelebs* (Common Chaffinch), *Periparus ater* (Coal Tit), *Phylloscopus collybita* (Common Chiffchaff), *Regulus regulus* (Goldcrest), *Sylvia atricapilla* (Eurasian Blackcap), *Turdus merula* (Common Blackbird), and *Turdus philomelos* (Song Thrush). For each species, five audio recordings corresponding to different individuals were selected and extracted from the MNHN online sound library (https://sonotheque.mnhn.fr). All recordings were deposited by J.-C. Roché, a professional ornithologist and field recorder, in various non-anthropogenic conditions using directional analog microphones, and were later digitized from their original magnetic band medium to lossless files in stereo.wav format with a 16-bit digitization depth and a 44.1 kHz sampling frequency. These recordings have been made with different recording setups, with potential different self-noise that we could not take into account. For each recording, the sixty seconds with the lowest amount of undesired sound events (such as bird songs from different species or individuals) were selected by an expert (EG).

### 3.3.2. Song extraction and level calibration

Bird songs are usually defined as the alternation of acoustic signals and silent intervals following specific temporal patterns. These patterns can be used for species identification. Here, a bird song was defined as a Region Of Interest (ROI), that is a frequency/time region of acoustic activity present in a solo bird recording (Ulloa et al., 2021). To be considered as a song, a sound event must have a minimum duration of 0.5 s. For two subsequent sound events to be labeled as distinct songs, there must be a “silent” interval of at least 0.5 s between the two successive sound events. We defined the repetition of successive songs from the same bird as a “singing session”, and the assemblage of multiple singing sessions from different birds (from the same or different species) as a “chorus”. Bird songs were automatically extracted using the segmentation scheme available in the Python package Bambird v0.3.0 (Michaud et al., 2023).

Each song was peak-normalized so that its maximum amplitude was equal to 1.0 in arbitrary amplitude units. Two types of amplitude

adjustment were then applied to each of these songs. The first volume adjustment aimed at reproducing interspecific disparities. For each species, the peak level at 1 m in dB sound pressure level (SPL) was taken from a previous literature review (Sueur et al., 2011). The amplitude of the songs for each species was then scaled by the maximum level of 100 dB SPL at 1 m as estimated for *Turdus philomelos*, the loudest species in our selection. The second amplitude adjustment aimed to reproduce disparities in level between songs from the same recording. The amplitude of each recording song was estimated (before normalization) and scaled by the maximum song level of the recording.

This calibration process allowed to control for song level and thereby increased Type-1 ecological validity of assembled soundscapes, even if it was more appropriate for species with low amplitude dynamics. Moreover, the various distances at which birds were recorded in the field resulted in sound propagation effects (attenuation and reverberation) which could not be removed from the calibrated song file. Additionally, each song file still carried a part of the background ambient sound in which it was recorded, although cropped to the time/frequency window of the song ROI. This unwanted background sound could not be distinguished when added to the background channel, but can be spotted in a “no background” (see Section 3.4.5) situation, or when analyzing sounds in the spectro-temporal domain (Fig. 2).

### 3.3.3. Temporal overlap avoidance algorithm

Birds show different temporal singing strategies depending on environmental and social contexts (Todt and Naguib, 2000), ranging from song overlap (Malavasi and Farina, 2013) to temporal avoidance at various time scales (Mondémé, 2022; Planqué and Slabbekoorn, 2008; Suzuki and Cody, 2019). Temporal Overlap Avoidance (TOA) is a general class of problems encountered in both biological and artificial systems such as internet protocols (Sadahiro, 2022; Suzuki et al., 2012). Here, we designed a self-organizing, asymmetric, temporal avoidance strategy to take into account such aspects of bird singing behavior and increase the ecological validity of our assembled soundscapes.

To describe this strategy in a comprehensible way, we first defined the Inter-Song Time Interval (ISTI) as the duration between the end of a song and the start of the following one. The ISTIs list for each species was extracted based on the time segmentation returned by the Bambird python package (Michaud et al., 2023), and the species ISTI means and standard deviations were estimated based on these empirical (observed) distributions (Table 1). The ISTI values used in the following algorithm were randomly selected among the lists of ISTIs which were collected using the Bambird package. These lists of ISTIs are now referred to as “observed ISTI species distributions”.

Evascape's TOA strategy mainly takes inspiration from the “DESYNC” algorithm (Degesys et al., 2007), which was first used as a basis for a TOA algorithm by Suzuki et al. (2012). This algorithm is based on the hypothesis that singing birds can predict other birds' next singing timing, and sing in between other birds' songs (Brumm, 2006; Cody and Brown, 1969; Suzuki and Cody, 2019). In Evascape, an updated version of this algorithm was used when multiple birds were to be present in the assembled soundscape. Still, it is important to acknowledge that there is no evidence that all bird species present in this study follow a TOA rule, implying some simplification in the algorithm process. In consequence, our TOA algorithm consisted in the application of the following rules:

(1) If the focal bird song did not overlap with any other bird songs, then the focal bird next ISTI was randomly chosen among the observed ISTI distribution of the species. When there was only one bird in the soundscape assemblage, this rule was constantly applied.

(2) When the focal bird song overlapped with at least one other bird song, the focal bird next song start was based on the predicted next song start of the overlapping bird. The overlapping bird next song start time was calculated following:

$$t_{start,focal}(next) = t_{end,overlapping}(current) + ISTI_{overlapping}$$
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Assembled chorus of four different species of birds (*Fringilla coelebs*, *Phylloscopus collybita*, *Sylvia atricapilla* and *Turdus philomelos*) represented (A) as a timeline diagram, and (B) as the Power Spectrum Density (PSD) of the corresponding singing sessions. The behavior algorithm produces a temporal dynamic where songs overlap more at the beginning and less at the end of the assembled soundscape.</figcaption></figure>

<caption>**Table 1**</caption>

Temporal characterization of birds songs from the Risoux forest's most prevalent species. Song duration is defined as the time interval between the start and the end of a bird event. Observed ISTI (Inter-Sing Time Interval) means were based on onsite recordings, while simulated ISTI means were based on the Evascape database. Results are given as mean ± standard deviation (σ).

<table><thead><tr><th>Species</th><th>Song duration<br>mean (s) (x ± σ)</th><th>Observed ISTI<br>mean (s) (x ± σ)</th><th>Simulated ISTI<br>mean (s) (x ± σ)</th></tr></thead><tbody><tr><td><i>Erithacus<br>rubecula</i></td><td>2.63 ± 1.15</td><td>5.33 ± 3.18</td><td>4.15 ± 2.67</td></tr><tr><td><i>Fringilla coelebs</i></td><td>2.24 ± 0.39</td><td>8.27 ± 2.78</td><td>6.27 ± 2.85</td></tr><tr><td><i>Periparus ater</i></td><td>1.71 ± 0.56</td><td>3.14 ± 2.27</td><td>2.28 ± 2.49</td></tr><tr><td><i>Phylloscopus<br>collybita</i></td><td>3.20 ± 1.18</td><td>5.69 ± 2.40</td><td>4.59 ± 2.14</td></tr><tr><td><i>Regulus regulus</i></td><td>2.51 ± 0.82</td><td>4.21 ± 1.43</td><td>3.42 ± 2.10</td></tr><tr><td><i>Sylvia atricapilla</i></td><td>3.64 ± 1.98</td><td>5.70 ± 3.64</td><td>3.98 ± 2.70</td></tr><tr><td><i>Turdus merula</i></td><td>2.07 ± 0.70</td><td>4.32 ± 2.44</td><td>3.23 ± 2.80</td></tr><tr><td><i>Turdus<br>philomelos</i></td><td>4.22 ± 6.15</td><td>3.14 ± 2.29</td><td>1.85 ± 3.45</td></tr></tbody></table>

where $t_{start,focal}(next)$ is the predicted overlapping bird next song start, $t_{end,overlapping}(current)$ is the overlapping bird current song end time and $ISTI_{overlapping}$ is the mean of the overlapping bird species ISTI distribution. The focal bird next song start was calculated so the song occurred midway between the overlapping bird current song end and the overlapping bird next song start. This rule thus minimizes temporal overlap between the songs of individual birds between both identical and different species.

(3) If the focal bird song did overlap with more than one bird song located at different distances, a “reference” overlapping bird was selected based on its song amplitude, relative to the focal bird. The relative distances of the overlapping birds to the focal bird were calculated, and their relative amplitudes were approximated by applying Haupert et al. (2023) frequency-dependent attenuation function (see Section 3.3.5) to the overlapping song peak amplitudes, with respect to their relative distances to the focal bird. The song with the highest relative propagated amplitude was designated as the reference overlapping bird. This second rule – which depends on both song level and spatial proximity between birds – introduces an asymmetry in the relationship between birds, as found by Suzuki and Cody (2019).

(4) If the focal bird next ISTI resulting from step (3) exceeded the maximum ISTI observed in the species distribution, then the focal bird next ISTI was randomly chosen among the observed ISTI distribution of the species. This final rule is purely arbitrary. Alternative rules could be proposed.

The effects of the TOA algorithm are potentially complex and difficult to anticipate. However, it is reasonable to assume that such an

algorithm affects the amount of temporal overlap and the characteristics of the ISTI distributions (e.g., their marginal moments: mean, variance, skewness and kurtosis). Exploratory simulations were conducted to assess these effects.

The effect of temporal overlap was assessed by applying a one-way ANOVA on the total overlap duration of assemblages of two birds, for “interactive” choruses which were simulated with the Evascape TOA algorithm, and “uncoordinated” choruses where ISTIs were randomly taken from observed distributions. In line with our expectations, the implementation of the TOA resulted in reduction of temporal overlap by a factor of two for “interactive” choruses versus “uncoordinated” choruses [F = 66.42; p < 0.001]. The composition of the chorus (birds from the same species or from different species) did not affect significantly temporal overlap for both “interactive” choruses [F = 0.37; p = 0.54] and “uncoordinated” choruses [F = 2.98; p = 0.09].

The effect of TOA on species ISTI distributions was assessed by applying a two-way ANOVA comparing the “simulated” ISTI distributions of a database of choruses of at least two birds with the “observed” ISTI distributions introduced earlier. The two-way ANOVA with factors “ISTI origin” (observed or simulated) and “bird species” showed significant effects of species [F(7,317) = 21854.9; p < 0.05], ISTI origin [F (1,323) = 8.60; p < 0.05] and interactions between the two factors [F (7,317) = 3.08; p < 0.05]. This means first that whether they are observed or simulated, species indeed have different ISTI distributions, but also that the corresponding simulated and observed ISTI distributions of the same species are different from each other. Moreover, chorus size (total number of birds in the chorus) did not have any effect on ISTI distributions (see Supplementary material Table S1).

### 3.3.4. Singing session generation

All songs that were extracted from the same recording were considered as coming from the same bird. The library of a given bird's extracted songs was defined as a “song pack”. The bird's identity therefore corresponded to the song pack that was produced from a single recording following 3.3.2. As indicated above, a singing session is the sequence of songs from one bird over a certain period of time. The behavioral algorithm presented in 3.3.3. therefore produced one singing session per bird (Fig. 2). Consequently, when the behavioral algorithm produced a chorus of *n* birds, *n* song packs from the corresponding bird species were randomly selected without duplication.

To initialize each singing session, one song was randomly selected in each song pack. The bird that was the most likely to sing next was chosen depending on its species ISTI distribution. Once selected, the timecode of the bird's next song was calculated by respecting the avoidance rule (Section 3.3.3), and a song was randomly chosen within the bird's song pack without duplication. This process can lead to two edge effects.
First, at the beginning of each singing session, the bird could either start singing after the beginning of the assembled soundscape or just before and be cut in half as a consequence. Second, since birds song packs were produced from one minute long recordings and songs were selected without repetition, the calculated ISTI values could be systematically so short that there would not be enough songs in the song pack to fill out the whole singing session, leaving a silent period of a few seconds at the end of the assembled soundscape. Here, we considered that those edge effects were negligible considering the aims of the soundscape assembler, but a better outcome might be obtained by extracting bird songs from longer recordings of at least two minutes. Such long duration recordings of high quality do exist, but the greater the species richness and abundance, the more difficult it is to find such recordings in open access databases.

### 3.3.5. Spatial distribution of sound sources and sound propagation effects

For each singing session, the distance from the source to the receiver was randomly chosen according to a uniform distribution between 10 and 80 m. Since songs were normalized in order to correspond to the amplitude of the same bird singing at 1 m from a microphone, it was then possible to simulate distance to the bird and between birds by applying sound propagation effects. Here, sound attenuation was simulated by applying a frequency-dependent attenuation function that was inferred from onsite sound propagation experiments (Haupert et al., 2023).

This function does not implement reverberation effects which can also substantially alter the song. To our knowledge, attempts to simulate reverberation in natural areas have been limited to a maximum frequency of 1,500 Hz (Goestchel, 2023), suggesting that further work is warranted to simulate the effects of reverberation on bird songs.

## 3.4. Background channel

### 3.4.1. Source audio database

All recordings of the background channel were retrieved from the sound database built by the long-term ecoacoustic project held in the Risoux forest (Grinfeder et al., 2022b). As all background recordings were made using the same equipment, there was a consistent self-noise for all background recordings. Audio files obtained from the 1st to the 31st of July 2019 at the peak of bird activity were sorted into the following sound classes: (1) “rain” only, (2) “wind” only, (3) “aircraft noise” only and (4) “ambient sound” only, labeled as “silence” in Grinfeder et al. (2022a). The prevalence of each sound was previously estimated in the Risoux forest along a complete year of recording. The ambient sound was present in 14 %, rain in 21 %, wind in 36 %, and aircraft in 75 % of the audio files.

### 3.4.2. Geophony: rain and wind

Geophony recordings (that is wind and rain), were automatically sorted based on a Power spectral density (PSD) analysis using a previously developed rain detector algorithm (Bedoya et al., 2017). In order to adapt to “wind” classification, “wind” recordings PSD were calculated on a [0–500] Hz frequency range, whereas “rain” recording PSD were calculated on [600–1200] Hz frequency range. Geophony power categories were determined as: (1) “low” level geophony recordings corresponding to PSD levels below the third quartile (Q3) of their respective PSD dataset, (2) “medium” level geophony recordings corresponding to PSD levels between Q3 and Q3 + 1.5 * IQR (IQR being the interquartile range), and (3) “strong” level geophony recordings corresponding to PSD levels above Q3 + 1.5 * IQR. Q3 + 1.5 * IQR corresponds to the outlier detection rule, meaning that all “strong” geophony recordings were outliers.

### 3.4.3. Anthropophony: aircraft noise

Aircraft noise recordings were automatically sorted based on Root Mean Square (RMS) power. The RMS values showed a bimodal

distribution with a first mode at 0.045 and a second mode at 0.074. The lowest value between these two modes was chosen as a discrimination threshold. Recordings with RMS lower than this threshold were sorted to “light aircraft noise” category and recordings with a higher RMS were sorted to “strong aircraft noise” category.

### 3.4.4. Biophony: insect stridulation

Recordings with biophony only were individually listened to by an expert (JS). One category of solo insect recordings was identified: *Tetigonia viridissima* (Linnaeus, 1758), the great green bush-cricket.

### 3.4.5. Ambient sound

Ambient sound or “ambient noise” is often considered as a form of “quiet” condition or “silence”, that is a form of background noise which is deprived of relevant acoustic information (e.g., Grinfeder et al., 2022a). We previously defined ambient sound as the low amplitude sound mixture of biophony and geophony (and potentially anthropophony) where components are indistinguishable from each other (Grinfeder et al., 2022b). Here, ambient sound was used as a baseline background condition in order to study the effect of anthropophonic, geophonic or biophonic noise on recording classification (see Section 3.2.6).

### 3.4.6. “No background” category

In order to study the effect of ambient sound on recording classification, a “no background” category was produced by applying a ~50 dB intensity attenuation on ambient sound recordings. Therefore, the background channel signal should not be audible and assembled soundscapes with “no background” mainly contained signals from the bird channel. Yet, it is important to acknowledge that bird songs still contain some amount of ambient sound from their original recordings (Fig. 2). This category therefore shows limited Type-2 ecological validity. In this context, the “no background” condition can also be considered as a baseline condition that replicates the background of previous soundscape assemblers.

### 3.4.7. Level calibration and final assemblage

Mean SPL was estimated for each background channel recording by using successively functions spl.wav2leq and util.mean_dB from the Python package scikit-maad v1.4.3. (Ulloa et al., 2021), with a gain of 42 dB corresponding to the gain of the Wildlife Acoustics SM4 recorders deployed in the Risoux forest (Haupert et al., 2023). First, each recording was scaled according to their RMS power. Second, dB SPL variations between the average mean dB SPL of the background recording and the reference 100 dB SPL intensity were calculated and converted into linear ratios. Finally, the respective linear ratios were applied in order to keep the amplitude dynamic between each recording. The calibrated recordings were stored as lossless files in mono.wav format with a 44.1 kHz sampling frequency and 16-bit resolution.

Because each background condition were to be tested independently, the addition of different background categories (e.g. wind + rain) was not considered here. Moreover, since all background recordings also contained an amount of naturally present ambient sound, adding background sounds would have resulted in adding ambient sounds in an uncontrolled way. Each assembled soundscape therefore consisted in the addition of the bird and background channels (Fig. 3). The resulting audio files were then stored as a lossless mono wav file with a 16-bit resolution and a 44.1 kHz sampling frequency.

## 3.5. Test of ecoacoustic indices

An experimental database of assembled soundscapes was generated in order to test the influence of background types and biodiversity levels on the score of seven ecoacoustic indices. The database aimed to test bird choruses along two types of biodiversity: (1) “species richness” (= number of present species) was tested from 1 to 8 species, with one
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Evascape final assembling stages represented as Power Spectrum Density (PSD) or spectrograms. Each singing session corresponds to a different bird species (from top to bottom: *Turdus philomelos*, *Sylvia atricapilla*, *Phylloscopus collybita* and *Fringilla coelebs*). Singing sessions were generated using a Temporal Overlap Avoidance Algorithm and propagated at various random distances (between 10 and 80 m) using a frequency-dependent attenuation function. The background sound used in this example is an ambient sound.</figcaption></figure>

individual per species and (2) “species abundance” (= number of individuals from the same species) was tested from 1 to 5 individuals from the same species. For each chorus of the “species abundance” category, the species was randomly selected among the 8 possible, based on a uniform distribution. For the sake of simplicity, assemblages with “species richness” choruses will be designated in the remaining part of the article as “richness choruses”, while assemblages with “species abundance” will be designated as “abundance choruses”. Both chorus types were assembled with 11 background conditions (“no background”, “ambient sound”, three levels of “wind” strength, three levels of “rain” strength, two levels of “aircraft noise” strength and *Tettigonia viridissima*). In order to produce variability due to random bird distances and random bird species selection, each experimental condition was repeated 28 times. This is representative of a standard 7 day soundscape monitoring protocol with 4 recordings per hour (*Grinfeder et al., 2022a*). The first level of richness and abundance being confounded, this led to an overall database of 12 * 11 * 28 = 3,696 assemblages, each assemblage corresponding to a unique bird chorus. Each assemblage lasting 1 min, the complete database amounted to a total of 3,696 min, that is 61 h and 36 min of assembled soundscapes.

Seven ecoacoustic indices were chosen. Two indices, nROI and aROI are based on ROI automatic identification (Ulloa et al., 2021). nROI and aROI stand for “number of ROI” and “ROI area”, respectively. Five other ecoacoustic indices were chosen for being often used in both research and environmental management and for being representative of different index families based on spectrographic and spectral analyses respectively (Quinn et al., 2023; Zhao et al., 2019): the Acoustic Complexity Index (ACI) (Pieretti et al., 2011), Acoustic Entropy (H) (Sueur et al., 2008), the Normalized Difference Soundscape Index (NDSI) (Kasten et al., 2012), the Acoustic Diversity Index (ADI) (Villanueva-Rivera et al., 2011) and the Bioacoustic Index (BI) (Boelman et al., 2007). Acoustic indices were separated into two categories based on their initial design. Indices which were explicitly coined to be driven by changes in biodiversity levels only (whether it is species richness or species abundance) were labeled as “biodiversity indices”. Other indices which were proposed to assess changes of soundscape components were

labeled as “soundscape indices”. The indices BI, H, ACI, nROI and aROI were labeled as “biodiversity indices”, while NDSI and ADI were labelled as a “soundscape index”. Still, it is important to state that even if “biodiversity indices” were designed to assess changes in biodiversity levels, previous studies showed that the link between biodiversity level and index score could be weak, at least up to this point (Alcocer et al., 2022; Sethi et al., 2023).

## 3.6. Statistics

Due to the non-normality of the data (with or without non linear data transformations) and non-validity of linear models, the relationship between ecoacoustic indices and richness or abundance was tested for each condition using the nonparametric Spearman correlation test. A Bonferroni correction was applied on type I error risk to compensate for the inflation risk of incorrectly rejecting a null hypothesis. The correction was applied for each index, that is for 22 correlations (2 biodiversity types and 11 background conditions), leading to a significance level $\alpha$ of 0.05/22 = 0.0023.

## 4. Results

Patterns of correlation between index scores and actual biodiversity varied depending on ecoacoustic index, background condition and biodiversity type (Table 2, Figs. 3-4). Each index was significantly correlated to biodiversity for a higher number of background conditions when the biodiversity type was “species richness” than “species abundance”. nROI and aROI indices were more frequently correlated with biodiversity (20 experimental conditions out of 22 for nROI, 19 experimental conditions out of 22 for aROI) than other indices, irrespective of biodiversity type. The H index was the least frequently correlated with biodiversity (4 experimental conditions out of 22). Rain, light, medium or strong, was the most impacting background condition, with no significant correlation for 31 out of 36 experimental conditions.

More specifically, the nROI index was significantly and positively correlated with richness in all background conditions. nROI was also
<caption>**Table 2**</caption>

Spearman coefficient of correlation between ecoacoustic index and chorus size for varying levels of species richness (top) and species abundance (bottom) and different background conditions. Strongest coefficients for each pair of index and biodiversity type are underlined. A star (*) indicates that the test was significant at a level of 0.0023, after a correction of Bonferroni applied to an initial type-I error risk of 0.05.

<table><thead><tr><th></th><th>Background sound</th><th>nROI</th><th>aROI</th><th>ACI</th><th>H</th><th>NDSI</th><th>ADI</th><th>BI</th></tr></thead><tbody><tr><td rowspan="10">Richness</td><td>No background</td><td>0.79*</td><td>0.70*</td><td>-0.08</td><td>0.47*</td><td>0.00</td><td>0.40*</td><td>0.57*</td></tr><tr><td>Ambient sound</td><td>0.71*</td><td>0.61*</td><td>0.57*</td><td>-0.22*</td><td>0.49*</td><td>0.61*</td><td>0.62*</td></tr><tr><td>Light rain</td><td>0.56*</td><td>0.44*</td><td>0.34*</td><td>-0.00</td><td>0.22*</td><td>-0.07</td><td>0.24*</td></tr><tr><td>Medium rain</td><td>0.39*</td><td>0.33*</td><td>0.06</td><td>0.11</td><td>0.10</td><td>0.01</td><td>0.13</td></tr><tr><td>Strong rain</td><td>0.40*</td><td>0.36*</td><td>0.01</td><td>0.10</td><td>0.30*</td><td>0.09</td><td>0.06</td></tr><tr><td>Light wind</td><td>0.77*</td><td>0.67*</td><td>0.62*</td><td>-0.12</td><td>0.51*</td><td>0.62*</td><td>0.66*</td></tr><tr><td>Medium wind</td><td>0.76*</td><td>0.63*</td><td>0.58*</td><td>0.12</td><td>0.49*</td><td>0.55*</td><td>0.55*</td></tr><tr><td>Strong wind</td><td>0.70*</td><td>0.61*</td><td>0.59*</td><td>0.17</td><td>0.53*</td><td>0.50*</td><td>0.50*</td></tr><tr><td>Light aircraft noise</td><td>0.74*</td><td>0.65*</td><td>0.63*</td><td>0.06</td><td>0.29*</td><td>0.56*</td><td>0.68*</td></tr><tr><td>Strong aircraft noise</td><td>0.80*</td><td>0.69*</td><td>0.64*</td><td>0.05</td><td>0.35*</td><td>0.60*</td><td>0.67*</td></tr><tr><td>Tettigonia viridissima</td><td>0.80*</td><td>0.71*</td><td>0.61*</td><td>-0.27*</td><td>0.29*</td><td>0.55*</td><td>0.48*</td></tr><tr><td rowspan="10">Abundance</td><td>No background</td><td>0.59*</td><td>0.57*</td><td>-0.18</td><td>0.40*</td><td>0.23</td><td>0.06</td><td>0.28*</td></tr><tr><td>Ambient sound</td><td>0.42*</td><td>0.34*</td><td>0.27*</td><td>-0.01</td><td>0.24</td><td>0.35*</td><td>0.34*</td></tr><tr><td>Light rain</td><td>0.29*</td><td>0.24</td><td>0.14</td><td>-0.05</td><td>0.12</td><td>-0.15</td><td>0.11</td></tr><tr><td>Medium rain</td><td>0.20</td><td>0.15</td><td>-0.07</td><td>0.12</td><td>0.09</td><td>0.06</td><td>-0.03</td></tr><tr><td>Strong rain</td><td>0.19</td><td>0.12</td><td>-0.03</td><td>0.05</td><td>0.15</td><td>0.09</td><td>0.07</td></tr><tr><td>Light wind</td><td>0.41*</td><td>0.34*</td><td>0.30*</td><td>0.00</td><td>0.24</td><td>0.37*</td><td>0.33*</td></tr><tr><td>Medium wind</td><td>0.48*</td><td>0.41*</td><td>0.38*</td><td>0.08</td><td>0.27*</td><td>0.33*</td><td>0.31*</td></tr><tr><td>Strong wind</td><td>0.43*</td><td>0.35*</td><td>0.35*</td><td>0.09</td><td>0.27*</td><td>0.16</td><td>0.22</td></tr><tr><td>Light aircraft noise</td><td>0.38*</td><td>0.36*</td><td>0.37*</td><td>0.05</td><td>0.11</td><td>0.30*</td><td>0.44*</td></tr><tr><td>Strong aircraft noise</td><td>0.54*</td><td>0.47*</td><td>0.45*</td><td>-0.06</td><td>0.12</td><td>0.33*</td><td>0.50*</td></tr><tr><td>Tettigonia Viridissima</td><td>0.50*</td><td>0.35*</td><td>0.27*</td><td>-0.11</td><td>-0.06</td><td>0.12</td><td>0.02</td></tr></tbody></table>

significantly and positively correlated with abundance in all background conditions, except for “medium rain” and “strong rain” (Table 2, Fig. 4). aROI was significantly and positively correlated with richness in all background conditions. aROI was also significantly and positively correlated with abundance in all background conditions, except for “medium rain” and “strong rain” (Table 2, Fig. 4). ACI was significantly and positively correlated with richness in all background conditions except for “no background”, “medium rain” and “strong rain”. ACI was significantly and positively correlated with abundance in all background conditions except for “no background” and all three “rain” background conditions (Table 2, Fig. 4). H was only significantly and positively correlated with richness in “no background” and “strong wind”, and negatively correlated with richness in the presence of *Tettigonia viridissima*. H was only significantly and positively correlated with abundance in “no background” (Table 2, Fig. 4). NDSI was significantly and positively correlated with richness in all background conditions except for “no background” and “medium rain”. NDSI was only significantly and positively correlated with abundance in “medium wind” and “strong wind” background conditions (Table 2, Fig. 5). ADI was significantly and positively correlated with richness in all background conditions except in all three “rain” background conditions. ADI was only significantly and positively correlated with abundance in “ambient sound”, “light wind”, “medium wind”, and the two “aircraft noise” background conditions (Table 2, Fig. 5). BI was significantly and positively correlated with richness in all background conditions except for “medium rain” and “strong rain”. BI was significantly and positively correlated with abundance in “ambient sound”, “light wind”, “medium wind”, and both “aircraft” background conditions (Table 2, Fig. 5).

## 5. Discussion

Over the past twenty years, ecoacoustics has been relying heavily on the production of increasingly larger sound databases, which would bring indices from complementary information to essential descriptors of soundscapes (Alcocer et al., 2022; Farina and Li, 2022). In order to understand how these indices would behave against a diversity of ecologically-valid scenarios, we introduced Evascape, an opensource soundscape assembler which was designed to reproduce recordings of bird choruses in natural soundscapes.

Based on the recordings and ecological data from a protected European cold forest, Evascape was used to generate a dataset of 3,696 assembled soundscapes, representative of various biodiversity levels and background conditions. The dataset was finally exploited to test five commonly used ecoacoustic indices (namely, ACI, H, NDSI, ADI and BI), and two more recent ones, nROI and aROI.

In general, these seven indices showed a better ability to detect changes in species richness than changes in species abundance. This is not a surprise as between-species acoustic disparities between bird vocalizations are expected to be greater than within-species acoustic disparities (Catchpole and Slater, 2003) and as most of these seven ecoacoustic indices were initially designed to assess species richness. Our study therefore indicates that ecoacoustic indices might not be recommended for population monitoring. Other methods, such as those based on sound source localization (Rhinehart et al., 2020), capture-recapture (Measey et al., 2017), or occupancy models (Welsh et al., 2013) should be preferred.

### 5.1. Assessing the influence of background sounds on ecoacoustic indices scores in Risoux proximal soundscapes

All indices were affected, at different levels, by background conditions leading to potential underestimation or overestimation of biodiversity levels. Notably, the “rain” conditions turned out to be the most challenging background condition for all indices. Water impacts on vegetation or ground produce multiple and unpredictable transient sounds, generating a white noise frequency profile. These sounds mask bird songs, preventing the detection of shifts in species richness or species abundance level. Overall, the presence of background conditions other than ambient sound limit the ability, and then the use by managers and stakeholders, of the indices to accurately assess biodiversity changes. This result is coherent with the current literature about the limitations of ecoacoustic indices (Alcocer et al., 2022; Llusia, 2024; Sethi et al., 2023). This sensitivity to changes in background sounds depends on the mathematical design of each index (Bradfer-Lawrence et al., 2023). Without getting into the details of each index computation, some explanations can be proposed to understand the behavior of the indices. For the sake of brevity, we chose here to focus mainly on the effects of rain.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Ecoacoustic indices nROI, aROI, ACI, and H as a function of chorus size for varying levels of species richness (1–8 species; left panels) and species abundance (1–5 birds; right panels). Ecoacoustic indices are computed for 11 background conditions. Vertical bars show standard error of the mean for 28 assembled soundscapes. Whiskers extend from the box to the farthest data point lying within 1.5x the inter-quartile range (IQR) from the box.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Ecoacoustic indices NDSI, ADI, and BI as a function of chorus size for varying levels of species richness (1–8 species; left panels) and species abundance (1–5 birds; right panels). Ecoacoustic indices are computed for 11 background conditions. Vertical bars show standard error of the mean for 28 assembled soundscapes. Whiskers extend from the box to the farthest data point lying within 1.5x the inter-quartile range (IQR) from the box.</figcaption></figure>
ACI overestimated biophony level in the presence of rain. The tran-
sient nature of rainfall sounds appears to have biased the values of ACI,
as this index was designed to capture short-term changes in amplitude.
The lack of correlation observed in ‘no background’ conditions may be
explained by ACI’s normalization process, which scales the maximum
spectrogram value, potentially mitigating the influence of increased
derivation from biophonic sources.

NDSI also showed high sensitivity to rain, but for a different reason. As NDSI performs a ratio between biophony and anthropophony, and since biophony is defined in this context as any sonic activity in the middle-frequency range [2–11] kHz and anthropophony in the low-frequency range [0–2] kHz, the prevalent presence of rain in the middle-frequency range resulted in mixing rain activity with biophonic activity. *Tettigonia viridissima* produces a stridulation above 5 kHz with a peak around 10 kHz which can influence the NDSI scores in a similar way.

In the same vein, BI involves measuring the sound level only in the frequency spectral range of bird songs, that is in the frequency range [2–8] kHz. Being mostly out of this frequency band, the stridulation of *Tettigonia viridissima* did not influence the results significantly, while the pink noise due to rain had an overestimating effect. Moreover, as BI encompass a normalization process similar to ACI, the value of BI is not only driven by the level of energy in the biophony frequency band but also by the maximum level of energy in the whole spectrogram. As soon as noise sources other than pink noise (i.e. rain) are introduced, the estimated biophony levels decrease relative to the ‘no background’ condition.

As the H index calculated entropy on the whole spectrum, the presence of any other sound other than bird songs resulted in an overestimation of biophony. Overall, H showed contradictory results, being positively correlated with species richness in the “no background” condition, and negatively correlated in the “ambient sound” one. This is consistent with the fact that the H index was originally designed and tested using simple chorus assemblages without any background (Sueur et al., 2008), similar to the “no background” condition. Although H may be useful in artificial and well controlled conditions (Zhao et al., 2019), it was not well suited for the assessment of acoustic biodiversity of the Risoux forest, and probably for most temperate forests.

ADI, an entropy-based “soundscape index”, was especially sensitive to rain. Rainy soundscapes, especially those with medium and strong rain, seemed to have “saturated” ADI scores, as the index was not able to capture the difference between a rainy soundscape with low biodiversity, and a rainy soundscape with high biodiversity. This is likely due to its normalization process, followed by a pixel count above a threshold, which can amplify the impact of transient noise components, such as rainfall. The normalization process common to all three indices, ADI, ACI, and BI, makes it challenging to predict their exact behavior.

Finally, only nROI and aROI were found to consistently detect
changes in biodiversity levels in “medium rain” and “strong rain”
background conditions. The indices nROI and aROI do not aim at
describing the frequency spectra as do NDSI, ADI and BI or at combining
temporal and frequency dynamics in the way of H and ACI. nROI and
aROI actually do not process signal analyses but rely on image tech-
niques applied to the spectrogram. First, the stationary noise is removed
from the spectrogram which mitigates the side effects of the rain, the
wind, the low frequency noise due to aircraft engine noise, and the
continuous, soft and low high frequency stridulation of *Tettigonia vir-
idissima*. Then, the segmentation of Regions Of Interest (ROI) is per-
formed using hysteresis thresholding binarization (Ulloa et al., 2021) to
ensure that contiguous pixels belong to distinct sound events. In addi-
tion, the indices nROI and aROI do not apply any signal scaling, as other
indices do, so that their results are comparable between different
recording conditions.

As NDSI and ADI were developed as “soundscape indices”, it was not
a surprise that they would be influenced by the presence of rain. More
specifically, despite the fact that NDSI was not driven by species

abundance, it was still partly driven by changes in species richness in
scenarios without rain or *Tettigonia viridissima*. ADI did show a sensi-
tivity to both background change and biodiversity level for species
richness and species abundance in non-rainy backgrounds. However, the
presence of aircraft noise and wind did not increase but lowered NDSI
and ADI scores compared to an ambient sound scenario. This suggests
that these indices are biased against certain background sounds,
lowering the proximal soundscape's score in their presence.

The same cannot be said for all ecoacoustic indices which were labeled as “biodiversity indices” as they did not all accurately reflect changes in biodiversity. Some “biodiversity indices” such as the H index seemed even to be less driven by species biodiversity and more driven by changes in background sounds than “soundscape indices”. nROI and aROI were found to be the most appropriate indices for the assessment of species richness and species abundance in the Risoux forest, even if their ability to assess species abundance is not reliable in a rainy scenario. Their robustness to the addition of aircraft noise is essential as the soundscape of the Risoux forest is highly polluted with air traffic noise (Grinfeder et al., 2022a). The sensitivity of nROI and aROI to species richness and species abundance, combined with their robustness to a diversity of background conditions, suggest that they could be used at least in ecological and acoustic environments similar to those of the Risoux forest. Altogether, and as illustrated in other contexts (Giuliani et al., 2024), these results demonstrate that “biodiversity indices” have downsides and upsides which depend on the type of biodiversity they are expected to measure and the background conditions in which they are likely to be applied. These effects should be assessed in other ecological contexts, in other closed habitats or in open habitats.

5.2. Potential uses and development

Evascape's current version allows for potential adaptations to various ecosystems. Given the right information about local biodiversity and the acquisition of quality recordings, Evascape can already produce engineered soundscape of various places which are dominated by bird songs. The most difficult technicality in that kind of scenario would revolve around the simulation of bird song propagation which can differ depending on the ecosystem in question. The Risoux current frequency-dependent attenuation function should therefore be used with caution. Moreover, in the context of a tropical-type ecosystem where insects and other animals are susceptible to occupy more of the proximal soundscape, substantial changes should be made so that Evascape takes into account all biophony as foreground elements. Different biophonic channels should be considered for each biophony group, plus an additional channel for background sounds.

Evascape offers the opportunity to produce soundscape assemblages of the past by digging into ancient biodiversity inventories, or of the future by investigating prospective models. For example, it would be possible to listen to the potential impact of climate change on different types of proximal soundscapes. Indeed, specific improvements in Evascape could help lead to long-term simulations of proximal soundscapes. This kind of simulation algorithm would also allow to unveil and test general laws in ecoacoustics such as the acoustic niche hypothesis (Kadish et al., 2019; Masumori et al., 2020). As Lawton (1999) stated, any attempt at finding universal laws in ecology often ends up forgetting the intrinsically localized nature of this field. But Lawton also suggested that a macro-ecological approach, with big databases that are representative of a wide variety of ecosystems, might bring fruitful insights. In this spirit, the Evascape assembler could be combined with a workflow that would extract biodiversity information from various terrestrial soundscapes around the world (Morrison et al., 2021).

Transforming Evascape into an actual simulation tool would require
a more detailed plan of action which would extend far beyond the scope
of this article. Still, we would like to address a current pitfall of Eva-
scape: the audio processing in the bird and background channels which
are currently independent. Yet, geophony and anthrophony have an
impact on bird singing behavior, influencing song initiation and acoustic traits (e.g., power or spectro-temporal content) (Grinfeder et al., 2022b; McLaughlin and Hansjoerg, 2013; Roca et al., 2016). One could argue that this should result in poor Type-1 ecological validity, but ecological validity should always be tied to the experimental question that is at stake. Here, the assembler was built to assess the impact of background sounds on biodiversity monitoring. To answer this question, it was therefore necessary to display the combination of many bird singing and noisy-like backgrounds, even if this scenario is unlikely to happen in real conditions. In the case of long term proximal soundscape simulation however, additional rules should be added so that birds behave differently in the event of a windy or rainy weather.

Additionally, soundscape engineering shows great potential for conducting psychoacoustical experiments in human and non-human animals, as suggested by recent research on human auditory perception of soundscape recordings (Apoux et al., 2023; Grinfeder et al., 2025; Lorenzi et al., 2023; Thoret et al., 2020). This suggests that Evascape could be used by non-ecoacousticians researchers, widening its potential uses and increasing its evolution potential even more so.

There is no doubt that despite the numerous conceptual and technical advancements that Evascape represents, there is a lot of room for improvement. It is not possible to display here every other potential adaptations and improvements that are susceptible to be implemented in the future, but this should be taken as an indication of Evascape's adaptation potential rather than a pitfall. We hope that ecoacousticians and other soundscape researchers will use Evascape as a playground to explore this potential and go beyond why Evascape was originally built for.

### 5.3. Conclusion

As pointed out by Llusia (2024), ecoacoustic indices should not be considered as a universal solution for acoustic biodiversity monitoring. The success of ecoacoustic indices varies from one ecosystem to another (Alcocer et al., 2022; Giuliani et al., 2024), suggesting that soundscape assemblage and test bench of ecoacoustic indices should be performed each time. Although Evascape offers a soundscape assemblage workflow that can be adapted to produce engineered soundscapes for other ecosystems, such an adaptation might require extensive modifications.

In its current form, Evascape should not be considered as a universal tool for soundscape assembling, but rather as a set of methodological principles that should be adapted to each project goal. If done properly, such a methodology could help building soundscape assemblers that would serve as reference to tune acoustic indices to the studied area. More generally, the current approach paves the way for setting up empirical or computational protocols with tight control of soundscape parameters, allowing for running full experiments where important ecological factors can be manipulated independently “all things being equal”, rather than quasi-experiments where important ecological factors are confounded. In summary, Evascape’s strong ecological validity and customization potential should help ecoacousticians, neuro-ethologists, bioacousticians, environmental acousticians and hearing scientists to build better tools for acoustic biodiversity analysis.

### CRediT authorship contribution statement

**Elie Grinfeder:** Software, Conceptualization, Methodology, Writing – original draft. **Christian Lorenzi:** Writing – original draft, Funding acquisition, Supervision, Conceptualization. **Yann Teytaut:** Writing – original draft. **Sylvain Haupert:** Visualization, Software, Writing – original draft, Validation. **Jérôme Sueur:** Conceptualization, Supervision, Writing – original draft, Formal analysis.

### Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

We are grateful to Richard McWalter for his valuable help that led to significant improvement of the manuscript, as well as to Marie-Pierre Reynet and Julien Barlet for their insight concerning the Risoux forest. We are also grateful to Quentin Goestchel for his support on the simulation of sound propagation in natural areas. We thank all three referees for their valuable comments. This study was partly funded by a grant from the Parc Naturel Régional du Haut-Jura (Région Bourgogne-Franche-Comté, Région Auvergne-Rhône-Alpes, Direction Régionale de l'Environnement, de l'Aménagement et du Logement de Bourgogne-Franche-Comté).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2025.113882.

### Data availability

Data will be made available on request.

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