Acoustic Monitoring of Forest Restoration for the Western Capercaillie

Acoustic Monitoring of Forest Restoration for the Western Capercaillie | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2025 V1 Latest version Share on Acoustic Monitoring of Forest Restoration for the Western Capercaillie Authors : Thomas Betton , Kévin Foulché , Emmanuel Menoni , Claude Novoa , Florence Nicole , Mark H VAN NIEKERK , Nicolas Mathevon , and Frederic SEBE 0000-0003-0264-9519 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174341564.46675632/v1 366 views 179 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Habitat restoration is an ecological management tool used to address the erosion of biodiversity caused by human activities. In this study, we examined the effect of restoration work in temperate highland forests, carried out to restore habitats for capercaillie Tetrao urogallus, a bird species known to be highly sensitive to habitat loss, fragmentation and anthropogenic disturbance. We used automatic audio recorders to investigate the daily and seasonal dynamics of acoustic biodiversity in recently restored temperate forests and compared them to unrestored control sites. From the recorded soundscapes, we compared the acoustic entropy difference (dH) and the acoustic dissimilarity (D) in three different habitat types: pure beech forests, mountain pine forests with dense rhododendron understories, and a mature mixed fir-beech forest. The dH index revealed an increase of the soundscape entropy or complexity in the restored habitats regardless of forest type, which may reflect a diversification of species present in the restored forest habitats. The D index revealed significant acoustic dissimilarity between control and restored montane pine forests, for which the new microhabitats may support greater spatial heterogeneity of biodiversity. The dH index also revealed an increase in bioacoustic entropy throughout the recording season at most sites (during spring/summer), with lower daily variability in bioacoustic entropy at restored sites compared to control sites, particularly in the afternoon. These results suggest that long-term bioacoustics monitoring can provide data that support forest conservation efforts conducted over large spatial and temporal scales. Title page Acoustic monitoring of forest restoration for the Western Capercaillie Abstract Habitat restoration is an ecological management tool used to address the erosion of biodiversity caused by human activities. In this study, we examined the effect of restoration work in temperate highland forests, carried out to restore habitats for capercaillie Tetrao urogallus , a bird species known to be highly sensitive to habitat loss, fragmentation and anthropogenic disturbance. We used automatic audio recorders to investigate the daily and seasonal dynamics of acoustic biodiversity in recently restored temperate forests and compared them to unrestored control sites. From the recorded soundscapes, we compared the acoustic entropy difference (dH) and the acoustic dissimilarity (D) in three different habitat types: pure beech forests, mountain pine forests with dense rhododendron understories, and a mature mixed fir-beech forest. The dH index revealed an increase of the soundscape entropy or complexity in the restored habitats regardless of forest type, which may reflect a diversification of species present in the restored forest habitats. The D index revealed significant acoustic dissimilarity between control and restored montane pine forests, for which the new microhabitats may support greater spatial heterogeneity of biodiversity. The dH index also revealed an increase in bioacoustic entropy throughout the recording season at most sites (during spring/summer), with lower daily variability in bioacoustic entropy at restored sites compared to control sites, particularly in the afternoon. These results suggest that long-term bioacoustics monitoring can provide data that support forest conservation efforts conducted over large spatial and temporal scales. Keywords Western Capercaillie, Habitat restoration, biodiversity response, ecoacoustic, biodiversity indicators, conservation. Main text Introduction Habitat restoration is an ecological management tool used to limit the erosion of biodiversity caused by human activities (Magurran, 2004; Goudie, 2013). The difficultly of ensuring effective restoration is an important challenge in forest ecology and management (Barbati et al., 2014, Sayer et al., 2004), which is particularly true for habitat restoration projects targeting endangered species or keystone species that play a crucial role in ecosystem functioning (Garibaldi and Turner, 2004). While biodiversity assessment is key for setting conservation priorities (Paillet et al., 2010), measuring the effect of habitat restoration on biodiversity remains difficult (Magurran, 2004). Biodiversity assessment must be economically realistic, reliable and replicable on a large scale to provide comparable results across sites and studies. A multitude of methods exist to measure biodiversity: exhaustive approaches based on the capture and identification of most living organisms within a studied habitat (Gewin, 2002), or methods focused on one or a few taxa considered as biodiversity indicators (Oliver and Beattie, 1993; Pearson, 1994). For instance, using umbrella species can provide first-hand information on biodiversity as a whole (Lawton et al., 1998). However, these approaches can generate habitat disturbance (Hill, 2005; Sutherland, 2011) and are extremely time-consuming. They offer little temporal repetition and thus do not address the need for temporal sampling that is essential for evaluating the success of habitat restoration. Thus, there is a current need to develop lower disturbance methods that provide a reliable and comprehensive view of biodiversity at restored sites. One approach to assessing biodiversity is to conduct an inventory of acoustic signals emitted by animals. By taking advantage of the diversity of sounds produced by several wildlife species (e.g. birds and insects: Bradbury and Vehrencamp, 1998), a survey of acoustic communities can provide a reliable estimate of biodiversity at a site (Rempel et al., 2005; Villanueva-Rivera, 2013). While direct assessment of sonorous species by human observers is still widely used (Ralph et al., 1995; Crouch and Paton, 2002), the use of Autonomous Recording Units (ARUs) is rapidly expanding (Marin-Cudraz et al., 2019), and Passive acoustic monitoring (PAM) has become an important tool in ecological studies, particularly within the expanding discipline of ecoacoustics (Sueur and Farina, 2015). This field focuses on creating acoustic metrics to serve as indicators for understanding the spatio-temporal dynamics of populations, species characteristics, community structures, and ecosystem functions (Sugai et al., 2019; Turlington et al., 2024). By recording the entire soundscape over long periods of time, ARUs generates huge amounts of data (Acevedo and Villanueva-Rivera, 2006), allowing for improved sampling effort over time. This type of passive, automated data collection reduces both the human effort required (Penman et al., 2005) and habitat disturbance during data collection (Diwakar et al., 2007). ARUs can be used to identify the species that comprise an acoustic community (Bardeli et al., 2010; Priyadarshani et al., 2018; Kvsn et al., 2020), or in a broader sense to calculate bioacoustic indices of biodiversity without requiring species identification (Sueur et al., 2008a; Towsey et al., 2014b; Pieretti et al., 2011; Sueur et al., 2014; Parks et al., 2014). The effectiveness of using acoustic indices to determine avian species richness in audio-recordings of the environment has been demonstrated (Towsey et al., 2014a; Wimmer et al., 2013), and compared to the traditional method of monitoring bird communities (Digby et al., 2013). In fact, the analysis of global environmental sounds is a rapidly expanding field with great potential for biodiversity monitoring, with over 60 acoustic indices developed to date (Buxtonet al. 2018, Bradfer-Lawrence et al., 2019). All of these indices reflect distinct attributes of the soundscape, and various facets of biodiversity (Sueur et al., 2014; Parks et al., 2014; Buxton et al., 2018a ; Buxton et al., 2018b; Borker et al., 2019). Acoustic indices offer a promising sollution but their inconsistent performance, highlight the need for cautious use and their targeted use (Alcocer et al 2022). Acoustic indices have been used to analyze and compare the diversity of different ecosystems such as tropical and temperate forests (Depraetere et al., 2012; Gasc et al., 2013; Rodriguez et al., 2014), grasslands (Shamon et al., 2021), coral and temperate reefs (Bertucci et al., 2016; Harris et al., 2016), artic landscape (Oliver et al., 2018), and focused on different animal communities (e.g. birds : Mammides et al., 2017 ; Buxton et al., 2018a; Bradfer-Lawrence et al., 2020; Oliver et al., 2018; bats :Bas et al., 2017 ; Gibb et al., 2019; anurans :Sousa-Lima et al., 2018 ; Indraswari et al., 2020; insects Hart et al., 2015 and fish Bertucci et al., 2016; Bolgan et al., 2018). In addition to the qualitative and comparative aspect of these methods, analysis of these indices provides the opportunity to track temporal changes in habitat and animal communities (Towsey et al., 2014b). In our study, we focused primarily on indices that allow us to characterize habitat differences in vegetation structure (Pekin et al., 2012; Pijanowski et al., 2011), habitat heterogeneity (Fuller et al., 2015), and habitat fragmentation (Tucker et al., 2014). Of the various indices that have been developed to date to quantify biodiversity, the entropy H index and the dissimilarity D index have been recognized as both reliable and complementary (Sueur et al., 2008a). The advantages of these two indices are that they use the Shannon index, currently one of the most widely used indices of diversity in ecology, as well as the mathematical concept of quantifying the information content of the signal. This quantification has been formalized within the framework of Shannon’s Mathematical Theory of Communication for a long time (Shannon and Weaver, 1949), and is a proven method currently used in bioacoustics analysis (Garcia et al., 2020). Acoustic entropy (H-index: Sueur et al., 2008a) is considered a proxy for the community biodiversity ( alpha index: Whittaker, 1972). In addition, the difference between the acoustic structure of signals from two study sites (dissimilarity index D: Sueur et al., 2008a), is a proxy for the dissimilarity between the two communities ( beta index: Whittaker, 1972). Sueur et al. (2008a) successfully tested such bioacoustic indices in a tropical forest and found that degraded habitat had lower acoustic diversity (lower H index), and altered community composition (high D index), compared to an undisturbed control area. Although the use of bioacoustic indices is rapidly increasing in a variety of habitats, their application remains limited for assessing the success of restoration strategies in terms of biodiversity gains (Znidersic and Watson, 2022; Robinson et al., 2023;Spatharis et al., 2024). In the present study, we examined the effect of restoring the mountain forests, located along the ridges of mountainous areas in temperate Europe, using acoustic biodiversity indices. These forests correspond to the habitat of the western capercaillie Tetrao urogallus. Our study sites were located in a protected area of the French Pyrenees, where efforts have been made to conserve the habitat of the western capercaillie (Castel, 2009). The capercaillie is the largest member of the grouse family. It is an iconic bird species of both ecological and cynegetic interest (Castel, 2009). The western capercaillie is associated with natural and old-growth forests (Mikoláš et al., 2017), with an open canopy and rich ground cover vegetation (Graf et al., 2005). Capercaillies are highly sensitive to habitat loss, anthropogenic disturbance, and fragmentation (Mikoláš et al., 2017). Because these habitat requirements are shared by many species associated with natural forests (Suter et al., 2002; Pakkala et al., 2003), capercaillies are considered a good example of an umbrella species and are receiving increased attention from forest managers as an indicator species for forest health. In an effort to return to forest conditions favorable to the capercaillie conditions and improve habitat openness and habitat heterogeneity (Menoni and Corti, 2000), forest managers have been conducting restoration efforts for about 20 years in two different forest habitats, namely beech forests ( Fagus sylvatica) and mountain pine forests ( Pinus uncinata ) with dense Rhododendron ferrugineum understories. Restoration work involved clearing small patches of habitat by reducing the abundance of dominant plant species and by releasing seedlings and saplings of less common species. Additional planting of previously present species also occurred, which is consistent with restoration efforts (Castel, 2009) that focus recreating formerly existing natural habitats (Volis, 2019). In comparable habitats that have not been subjected to past management pressures, the understory is a mosaic of vegetation, which forest managers have attempted to mimic. In such environments, an increase in mosaicism in the understory could be expected to help enhance biodiversity. To measure habitat restoration in terms of biodiversity, we assessed acoustic biodiversity using a refined entropy index dH, based on the Shannon index H’ and the acoustic dissimilarity index D. The dH index was determined from the the H index calculated on the signal sequences minus the H index of the residual background noise, to limit the impact of noise on biophonic measurements (Figure 2S in supplementary method). In addition to assessing the sources of variability in these acoustic biodiversity indices, and comparing their temporal dynamics across habitats, we considered daily and seasonal variations over a multi-month period from April to July 2017. We tested the following three hypotheses. Hypothesis 1: dH is higher in restored forests habitats compared to the control for beech and mountain pine forests, revealing a positive impact of habitat restoration on biodiversity; Hypothesis 2: D is higher in restored forest habitats than in control sites, highlighting greater spatial heterogeneity of biodiversity induced by the new microhabitats; Hypothesis 3: daily and seasonal dynamics of biodiversity captured by dH highlight greater diversity of birds (peak of acoustic diversity in the morning and early spring) and insects (peak of acoustic diversity in the afternoon and spring) in restored sites compared to control sites. Materials and methods Study sites The study was conducted at five sites in the French Pyrenees. Sites were selected based on their geographic proximity and restoration context (Figure 1a). Four of the sites were located in the Orlu National Hunting and Wildlife Reserve (RNCFS, mean latitude: 42.658938°, mean longitude: 1.965190°, 4.3 ha), a reserve that aims to contribute to the conservation of the Pyrenean wildlife. Sites 1 and 2 were located in beech forests; sites 3 and 4 were situated in mountain pine forests and a 5 th site was located within a mixed forest outside of the Orlu Reserve, in a valley 15 km from the other sites (see Figure 1S in Supplementary Methods for more information). The acidiphilous Atlantic beech forest of site 1, named “control beech forest” is strongly dominated by beech trees, Fagus sylvatica . This forest has not been logged since 1964. The trees exceed 25 cm in diameter, with a closed canopy and a dense litter layer that prevents the development of ground vegetation. Other tree species, birch Betula pendula , mountain ash Sorbus aucuparia , oak Quercus robur , and willow Salix caprea, are present in a very irregular pattern. This habitat likely structure developed as a result of past logging practices (Menoni et al., 2012), which precluded the establishment of western capercaillie due to the lack of singing and courtship areas caused by progressive closure of the vegetation. This is largely the result of rapid regeneration of beech trees after the last logging. In site 2, called “restored beech forest”, the forest was modified in order to recreate a mixed forest, supposedly more attractive for capercaillie and its associated species (forest-dwelling birds, mammal and insect species: Pakkala et al., 2003). The restoration work, carried out in 2002, consisted of creating forest clearings by clear-cutting small patches of beech (0.1 to 0.2 ha) spaced 50-150 m apart and retaining the cut trees in place (i.e., leaving the cut trees on the ground as in a windthrow), as well as retaining trees of interest and planting pine trees in the middle of the cut areas (Castel, 2009). The objective of these restoration operations was to allow the recreation of pre-existing natural habitats, which were mixed forests cleared by natural disturbances before being drastically transformed for charcoal production in the 18th and 19th centuries (Castel, 2009). The Pyrenean mountain pine forest at site 3, referred to as the “control mountain pine forest”, is a young subalpine hill-pine forest in the paraclimax stage (open stands on a ferruginous rhododendrons carpet, following the abandonment of overgrazing). In this habitat, the dense carpet of rhododendrons limits access for ground-dwelling fauna, such as the western capercaillie (Milhau, 2012). Site 4 is a 6 ha “restored mountain pine forest”. Originally identical to site 3, the site underwent a restoration program from 1999 to 2009, consisting of rhododendron mosaic clearing (Castel, 2009) to promote the presence of the western capercaillie and its associated species. Site 5, or natural site, is an old-growth “mixed forest” dominated by fir trees ( Abies alba ), scots pine ( Pinus sylvestris ) and beech ( Fagus sylvatica ). This forest has not been exposed to any management and presents typical and natural habitats for the western capercaillie. Its biodiversity is high and was used as a reference for the restoration of the beech forest in site 2. This site is located at a similar elevation, exposure, and soil conditions as the forests at sites 1 through 4. Acoustic recordings We used 12 audio field recorders Song Meter 4 (SM4: Wildlife Acoustics, 2009), with three recorders per site. As described later, not all sites were monitored during the same time period. Signals were digitized at a sampling frequency of 48 kHz (16 bits). The gain was set at 16 dB for all recorders. At each site, three recorders were placed to avoid ecotone effects, which can be found at the periphery of the study areas due to the ecological transition phenomenon, and abiotic noise sources in micro-habitats (e.g., sites close to the noise produced by small mountain streams during snowmelt and sites with high wind exposure). Using a GPS (Garmin GPS Map 64), the three recorders at each site were spaced 100 m apart, generating a 200 m linear transect perpendicular to the mountain slope for each site (Figure 1a). The linear transects were located at elevations ranging from 1495 m to 1897 m. (Table1). All recorders were placed on the east side of a tree trunk, at a height of 1.9 m and protected by PVC tubes to limit the impact of rain on the equipment. The alignment of the right and left microphones was perpendicular to the slope of the ground, with the right microphones oriented northwards. Temporal sampling Since we only had 12 different SM4s to monitor 5 sites, with three recordings per site, we had to rotate the different recorders and shift sampling in time between habitat types. We performed raw recordings from April 21 st to July 12 th , with the timing of the recordings varying according to habitat type (Table 1 ; Figure 1c). We performed the recordings of beech forests earlier in the season to avoid sounds from cattle bells. Recordings in the mountain pine forests could not begin until late May due to snow cover. All SM4 recorders were programmed to record during a total of 4 hours each day, from 1 h before to 1 h after sunrise (“morning” recording) and from 6h to 8h after sunrise (“afternoon” recording; Figure 1b). The morning period was chosen to account for dawn chorus activity (Stanley et al., 2016; Farina and Ceraulo, 2017; Da Silva and Kempenaers, 2017). Afternoon recordings were primarily made to measure bird and insect acoustic activity (Gogala and Riede, 1995; Thompson et al., 2017). This schedule produced a total of 1632h of raw recordings (Table 1 ). However, due to weather conditions (heavy rain and strong winds with acoustic amplitude greater than the amplitude of the signal of interest; see next section on signal pre-processing), we could only analyze the remaining recordings: 30 days corresponding to 360 h (56 % of raw recordings) for each beech forest site (site 1 and 2; Table 1), 28 days corresponding to 336 h (54 % of raw recordings) for each pine forest site (site 3 and 4; Table 1), and 11 days corresponding to 132 h (33 % of raw recordings; Table 1) for the mixed forest site (Table 1). For each remaining 4-h recording, we sampled 16 sequences of 150 s every 15 min (Depraetere et al., 2012). We labeled M1 to M8 the sequences extracted from the recordings performed in the morning, and A1 to A8 the sequences extracted from the recordings performed in the afternoon (Figure 1b). This sampling design allowed us to analyze 60 h of recordings for each beech forest site (10 % of raw recordings; Table 1), 56 h of recordings for each pine forest site (9 % of raw recordings; Table 1), and 22 h of recordings for the mixed forest site (6 % of raw recordings; Table 1). Signal pre-processing We analyzed only recordings made during suitable weather conditions (no rain or wind). For each recording sequence (M1 to A8), the signal-to-noise ratio (SNR) was calculated and we excluded the recordings whose SNR < 1 (root mean square computed on each sequence/ root mean square computed on each of the M1 sequences characterized by noise). The SNR calculation was coupled with a manual approach by examining the spectrograms and oscillograms, and listening to the sequences from M1 to A8 on each day to check the accuracy of the SNR information. In these cases, we also removed the same-day recordings from the corresponding control, restored and reference sites. We applied a first 0.2 - 24 kHz band-pass filter (R Seewave package 2017, fir function: Sueur et al., 2008b; Depraetere et al., 2012), to reduce the influence of low-frequency anthropogenic noise (mainly from aircraft, distant road traffic, and logging). We applied a second, more specific, spectral filter (R Seewave package 2017, ffilter function: Sueur et al., 2008b) to reduce the impact of natural background noise (caused by rivers, wind and/or rain). The average spectrum of a 150- s sequence including only natural background noise was used to apply a spectral filter on all recordings from the same recorder and day (see figure 2S of supplementary methods). The resulting sequences were referred to as “signals”. The average spectrum of the background noise sequence was also used to apply a spectral filter on this sequence itself (the remaining sequences were called “residual background noise”). The residual background noise corresponds to the part of the soundscape not removed by the specific spectral filter in the signal sequences (see figure 2S in Supplementary Methods to visualize the impact of these filters on the different signal spectra and background noise spectra). Indices of acoustic biodiversity A biodiversity index is a mathematical function that evaluates some aspects of animal communities (Sueur et al., 2014). As shown by Sethi et al (2020), the relevance of the acoustic index varies depending on the ecosystem studied. To test the relevance of the H and D indices in beech and mountain pine forests, we conducted several tests using artificial bird song choruses composed solely of known birds in the study forests (see figure 3S and 4S of the Supplementary Methods for more information). The objective was to determine the theoretical efficiency of acoustic indices to measure the acoustic diversity of singing species in the studied habitats. Calculation of 8 acoustic indices classically used in ecoacoustics (e.g. ACI, AE, D, M, NDSI, NP, H, SH from R Seewave package 2017: Sueur et al., 2008b; Sueur et al., 2014), on synthetic choruses of a variable number of bird species, showed that the H and D indices provide a good characterization of bird communities in beech and mountain pine forests (see figure 3S and 4S in Supplementary Method). Thus, among the different indices developed to date, we chose to use the H and D indices, as these two indices have been recognized as complementary and reliable under natural conditions (Sueur et al., 2008a). H is an α indices. It is a proxy for the number of sound-emitting entities and their relative abundance, while D is a β indices, which are proxies for the level of dissimilarity between entities. Alpha biodiversity acoustic index The H index reflects signal entropy in terms of time, frequency and/or amplitude ( H function in R Seewave package: Sueur et al., 2008b). The entropy of acoustic signals emitted by an animal community increases with the number of emitting species and their relative abundance. Moreover, the number of sympatric species is generally related to the level of habitat complexity. The H index can therefore be considered as a proxy for habitat complexity in terms of species richness and abundance. The H index is based on the Shannon index (H’): \begin{equation} \left(1\right)\text{\ \ \ \ H}^{\prime}=(\ -\ \sum\ (pi*\ln\ pi\ )/lnS\ \nonumber \\ \end{equation} where S = number of species and pi = proportion of individuals in the i th species abundance The temporal entropy (Ht) is obtained by applying Shannon’s formula to the Hilbert’s amplitude envelope. The spectral entropy (Hf) is obtained by applying Shannon’s formula to the sound frequency spectrum after a Short Time Fourier Transformation (STFT): \begin{equation} \left(2\right)\text{\ \ \ \ }\text{Ht}=\ -\ \sum\begin{matrix}n\\ t=1\\ \end{matrix}A\left(t\right)\text{\ x\ }\log_{2}\left(A\left(t\right)\right)\text{x\ }\log_{2}{(n)}^{-1}\nonumber \\ \end{equation}\begin{equation} \text{\ \ \ }\left(3\right)\text{\ \ \ \ }\text{Hf}=\ -\ \sum\begin{matrix}n\\ f=1\\ \end{matrix}S\left(f\right)\text{\ x\ }\log_{2}\left(S\left(f\right)\right)\text{x\ }\log_{2}{(n)}^{-1}\nonumber \\ \end{equation} where n = length of the signal in number of digitized points, A(t)= probability mass function of the amplitude envelope, S(f) = probability mass function of the mean spectrum calculated using an STFT along with the signal with a non-overlapping Hanning window of N = 512 points The H index is the product of Ht by Hf: \begin{equation} \left(4\right)\ \ \ \ H=Ht*\text{Hf\ }\nonumber \\ \end{equation} with Ht = temporal entropy and Hf = frequency entropy The H index varies from 0 to 1. Values close to 0 correspond to pure tones (sounds with minimal entropy) while values close to 1 imply high entropy. To optimize the assessment of acoustic entropy due to biodiversity, we firstly computed a raw H index Hs on the signal sequences (M1 - A8), and then a residual H index Hn from the residual background noise sequences (see signal pre-processing below). We then calculated the acoustic entropy difference (dH), as a fair index of acoustic biodiversity: \begin{equation} \left(5\right)\ \ \ dH=Hs-\text{Hn}\nonumber \\ \end{equation} where Hs = H calculated on signal sequences and Hn = H calculated on residual background noise sequences The dH index varies from -1 to 1. Values near -1 are obtained when the ambient sound is primarily due to non-biological sources (e.g. wind moving tree branches and leaves), and few or no animals are producing sounds (“silent communities”). Values close to 1 are obtained when most of the recorded sounds were produced by living animals (“active acoustic communities”). The relative contribution of signal and background noise on the calculation of the dH difference is presented in the Supplementary Results (see Table S1 and Figure 5S). For each recorder, we calculated the dH index for each sequence of the day (M1 to M8 and A1 to A8), resulting in 16 dH values per day. We averaged these 16 values to obtain an average dH value per recorder for a day, and then averaged the 3 average dH values from the recorders at the three sites. This program yielded an average dH value per day and per site. Beta biodiversity acoustic index The D index illustrates the level of difference between two acoustic communities as a function of time, frequency and/or amplitude ( D function in R Seewave package: Sueur et al., 2008b). The differences between two acoustic communities should increase with the number of unshared species between pairs of recordings. The D index can therefore be considered a proxy for dissimilarity between two animal communities. It is the product of temporal dissimilarity (Dt) and frequency dissimilarity (Df). The temporal dissimilarity (Dt) is obtained from the Hilbert’s amplitude envelopes of the two signals to be compared. The frequency dissimilarity (Df) is obtained from the frequency spectrum of the two signals: \begin{equation} \left(6\right)\text{\ \ \ }\text{Dt}=\ 0,5\times\sum\begin{matrix}n\\ t=1\\ \end{matrix}|A_{1}\left(t\right)-\ A_{2}\left(t\right)|\nonumber \\ \end{equation}\begin{equation} \left(7\right)\text{\ \ \ }\text{Df}=\ 0,5\times\sum\begin{matrix}n\\ f=1\\ \end{matrix}|S_{1}\left(f\right)-\ S_{2}\left(f\right)|\nonumber \\ \end{equation} where \(A_{1}\left(t\right)\) , \(A_{2}\left(t\right)\) = probability mass functions of the amplitude envelope for the two recordings under comparison and \(S_{1}\left(f\right)\) , \(2\left(f\right)\) = probability mass functions of the of the mean spectrum for the two recordings The D index is the product of Dt by Df: \begin{equation} \ \left(8\right)\ \ \ \ \ \ D=Dt*\text{Df\ }\nonumber \\ \end{equation} with Dt = temporal dissimilarity and Df = frequency dissimilarity The D index varies from 0 to 1. Values close to 0 indicate that the compared signals have the same acoustic structure (in time, amplitude and frequency), while values close to 1 underly a deep difference between the acoustic structure of the two signals. To limit computational time (Depraetere et al., 2012), we chose to perform D calculations only between M4 recording sequences, as preliminary results showed a daily peak of α biodiversity for this sampling. For each day analyzed and between pairs of M4 sequences, we computed intra-site D indices between acoustic communities of recorders placed at the same site, and inter-sites D indices between acoustic communities of recorders placed at different sites. We averaged the three D values per day for each comparison, to obtain an average D-value per intra-site or inter-sites comparison for a day. Statistical analysis For hypothesis 1 with H, we computed Linear Mixed-Effects Models to assess the fixed effect of site on dH, considering date and recorder as random factors (R lmer4 package, lmer function: Kuznetsova et al., 2017). The specific time sampling by habitat type resulted in the lack of a common recording period between the three sites (see temporal sampling). Thus, LMER models were built on three different subsets to exclude variations due to the phenology of species presence and activity. These subsets were used to measure: i) the effect of restoration in beech forests (site 1 vs site 2), ii) the effect of restoration in mountain pine forests (site 3 vs site 4) and iii) the effect of restoration in beech forests versus unmanaged mixed forest (site 1 vs site 2 vs site 5). Following the recommendations of Anderson and Burnham 2002, several formula syntaxes were tested for each comparison (see Table 2) which were subjected to model selection using AIC. The best model per habitat was selected using the lowest Akaike Index Criterion (AIC: Arnold, 2010). For hypothesis 2 with D, the site fixed effect was tested using LMER, with date as a random factor. Two subsets were analyzed separately to test the effect of restoration i) in the beech forest (D intersite between 1 & 2 vs D intrasite 1 vs D intrasite 2) and ii) in the mountain pine forest (D intersite between 3 & 4 vs D intrasite 3 vs D intrasite 4). For hypothesis 3 and to determine the fixed effects of period (dates), daily period (morning and afternoon) and daily time (M1 to A8 sequences), LMER models were constructed with dH as the response variable and recorder as the random effect. A total of four distinct subsets were used, namely: two subsets for beech forests i) in site 1 and ii) in site 2, and two subsets for mountain pine forests iii) in site 3 and iv) in site 4. The best models were selected using the lowest Akaike Information Criterion (AIC: Arnold, 2010). To complementary the previous analysis of daily period, we used LMER to compare the fixed effect of daily period (morning and afternoon) on dH between the control and restored beech forest, and between the control and restored mountain pine forest. Two subsets were analyzed separately: i) between the control and restored beech forest (site 1 vs site 2) and ii) between the control and restored mountain pine forest (site 3 vs site 4). In the selected models, the significance of explanatory variables was assessed using the Wald chi-square tests ( Anova function , R Car package). Post-hoc tests were performed to allow pairwise comparisons between fixed factors (Tukey with glht function: Ruxton and Beauchamp, 2008; R Multcomp package). Normality and homoscedasticity of samples were checked with Shapiro-Wilk and Bartlett tests, respectively. The significance level was set at 5 %. Results Habitat restoration increases acoustic biodiversity To test hypothesis 1 and assess the effect of retoration on dH, Linear Mixed-Effects Models were constructed with different syntaxes, considering date and recorder as random factors. The AIC of the best model by habitat type is summarized in Table 2. In all three habitats, restoration had a significant effect of increasing acoustic entropy dH (site 1 vs site 2: LMER syntax 1, anova: chisq=32.8, df=1, p=1.0*10-8; Figure 2a; site 3 vs site 4: LMER syntax 2, anova: chisq=173.9, df=1, p=2.20*10-16; Figure 2b; site 1 vs site 2 vs site 5: LMER syntax 3, anova: chisq=27.2, df=2, p=1.3*10-6; Figure 2c). All post-hoc test results are shown in Figure 2a, 2b and 2c. The level of acoustic entropy is higher in the restored sites, compared to the controls, but is still lower than the entropy observed in the unmanaged forest. Concerning hypothesis 2, LMER models run on D values in beech forests failed to show significant differences between intra- and inter-sites conditions (D intersite 1/2 vs D intrasite 1 vs D intrasite 2: LMER syntax 4, anova: chisq=3.2, df=2, p = 1.6*10 -1 ; post-hoc tests are displayed in Figure 2d). Conversely, D values were higher in recordings from the restored mountain pine forest than in recordings from the control mountain pine forest and than D values between recordings from both sites (D intersite 3/4 vs D intrasite 3 vs D intrasite 4: LMER syntax 4, anova: chisq=27.3, df=3, p=1.2*10 -6 ; post-hoc tests are displayed on Figure 2e). This result highlights greater spatial heterogeneity of biodiversity in the restored mountain pine forest. Habitat restoration impacts the temporal dynamics of acoustic biodiversity Concerning hypothesis 3, to assess the daily and seasonal dynamics of biodiversity, acoustic biodiversity measured by dH varied over the course of the recording season: from mid-April to mid-June at the two beech forest sites (site 1: LMER syntax 6, anova: chisq=229.8, df=29, p=2.2*10 -16 ; site 2: LMER syntax 6, anova: chisq=816.7, df =29, p=2.2*10 -16 ); from mid-May to mid-July at the two mountain pine forest sites (site 3: LMER syntax 7, anova: chisq=1363.1, df =27, p=2.2*10 -16 ; site 4: LMER syntax 6, anova chisq=163.4, df =27, p=2.2*10 -16 ). As shown in Figure 3b, both control and restored mountain pine forests showed an increase in dH in the first days of monitoring (between late April to early June). The dH values in the restored forest were always higher than the values in the control site with a relatively constant difference in dH values over time. In the control beech forest (Figure 3a), we also observed an increase in dH in the first days of monitoring (between late May and early June). Conversely, we noted a slight decrease in dH in the first days of monitoring for the restored beech forest. The difference between dH values for the control and restored beech forest decreased over time until reaching similar values. On a shorter timescale (i.e. dialy, sequences M1 to A), we found variability in dH at all beech forest sites (site 1: LMER syntax 6, anova: chisq=178.2, df=14, p=2.2*10 -16 ; site 2: LMER syntax 6, anova: chisq=973.6, df=14, p=2.0*10 -16 ; Figure 4a) and mountain pine forest (site 3 : LMER syntax 7, anova: chisq=920.3, df=14, p=2.0*10 -16 ; site 4 : LMER syntax 6, anova: chisq =391.0, df=14, p=2.0*10 -16 ; Figure 4b). For all sites, we observed a peak in acoustic entropy 15 minutes before sunrise, related to the bird chorus (Figure 4a and 4b). During the afternoon, while dH appears to remain stable at the restored sites, it decreases at the control site, with high intra-site variability (Figure 4a and 4b). The dH is significantly lower in the afternoon in the control and restored beech forest (site 1: LMER syntax 6, anova: chisq=12.4, df=1, p=4.3*10 -4 ; site 2: LMER syntax 6, anova: chisq=17.2, p=3.4*10-5), but not in the control mountain pine forest (site 3: LMER syntax 7, anova: chisq=2.0, df=1, p=1.6*10 -1 ). Conversely, in the restored mountain pine forest, afternoon dH is higher than morning dH (site 4: LMER syntax 6, anova: chisq=54.7, df=1, p=1.4*10-13). If we focus on the comparison of dH across the daily period (morning and afternoon) performed at the habitat level (Figure 5), it appears that dH differs significantly between morning and afternoon recordings in beech forests (site 1 vs 2: LMER syntax 5, anova: chisq=632.3, df=3, p=2.2*10 -16 ; Figure 5a) and in mountain pine forests (site 3 vs 4: LMER syntax 5, anova: chisq=1137.8, df=3, p=2.2*10 -16 ; Figure 5b). As illustrated in Figure 4, post-hoc tests performed on LMER show significantly lower dH values in the afternoon for the control beech forest (site 1: LMER syntax 5, Z=22.2, p=2.2*10 -16 ) and the restored beech forest (site 2: LMER syntax 5, Z=2.4, p=3.7*10 -2 ). But contrary to the previous model (syntax 7), in the control mountain pine forest, the afternoon dH is significantly lower than in the morning (site 3: LMER syntax 5, Z=19.0, p=2.2*10 -16 ). The post-hoc test also highlights that in the restored mountain pine forest, afternoon dH is significantly higher than morning dH (site 3: LMER syntax 5, Z=-13.5, p=2.2*10 -16 ). All post-hoc tests results are shown in Figure 5a and 5b for the beech forest sites and mountain pine forest sites, respectively. We found no significant differences in dH between control and restored sites in the morning (site 1 vs 2: LMER syntax 5, post-hoc test: Z=-1.8, p = 7.1*10 -2 ; site 3 vs 4: LMER syntax 5: LMER n°21: post-hoc test: Z=1.0, p = 3.0*10 -1 ). Conversely, in the afternoon the dH is significantly higher in the restored sites than in the control sites (site 1 vs 2: LMER syntax 5, post-hoc test: Z=18.1, p=2.2*10 -16 ; site 3 vs 4: LMER syntax 5, post-hoc test: Z=33.5, p=2.2*10 -16 ). Discussion Biodiversity improvement after habitat restoration The present study uses two bioacoustic indices to assess the impact of restoration measures on the biodiversity of two different forest ecosystems. Our data revealed: i) an increase in bioacoustic entropy (dH index) in restored habitats regardless of forest type, ii) significant dissimilarity (D index) between restored and control mountain pine forests (no significance was found between restored and control beech forests), iii) an increase in bioacoustic entropy throughout the recording season at most sites. These results are discussed in more detail below, highlighting both the advantages and disadvantages of monitoring biodiversity using bioacoustic indices. Species diversification may explain the increase in acoustic biodiversity in restored forest habitats A previous field analysis, at the same sites as the current study, showed that the total biomass of understory invertebrates increased fivefold and the abundance of saproxylic beetles was significantly higher in restored forest habitats, as the structure and composition of the vegetation changed. The restoration work carried out therefore had a significant impact on the taxa and the ecosystem as a whole (Menoni et al., 2006). The higher level of acoustic diversity and entropy (higher dH) observed in restored habitats suggests that these habitats support more complex biological communities than control sites (Sueur et al., 2008a). Although a higher dH can be partly explained by an increase in the number of individuals in an already present population, the presence of new species could likely be the cause of the diversification of an acoustic community. Such a causal relationship has been previously described in tropical forest (Depraetere et al., 2012). This community restructuring is consistent with previous observations based on direct assessment methods (census of species in the field) which showed that habitat restoration has a favorable impact on both vegetation (Vaisanen et al., 1993; Brakenhielm and Liu, 1998) and animal diversities (Desender et al., 1999; Menoni et al., 2006). Although restoration has a significant positive impact on the level of acoustic biodiversity, its level does not reach that observed in an unmanaged forest (mixed forest of the site 5). This reflects the fact that we only observed the premise of a change in animal and plant communities and we have not yet reached the ultimate stage of restoration comparable to the unmanaged forest (mixed forest of the site 5). This type of ecological restoration takes time, and in some cases, it may even be impossible to restore a forest with the same characteristics as a fully mature natural ecosystem, as certain ecological conditions and long-term processes cannot be easily replicated. New microhabitats may support greater spatial heterogeneity of biodiversity Connell’s Intermediate Disturbance Hypothesis (Connell, 1978) states that sufficient disturbance facilitates the establishment of early-stage species in late-stage formations, thereby increasing diversity, whereas excessive disturbance is intolerable for some species and will reduce the diversity of early-stage communities (Sheil and Burslem, 2013). The higher spatial heterogeneity of acoustic biodiversity observed in the mountain pine forest is likely a consequence of restoration-induced changes in spatial structure of the habitat. The clearing of rhododendrons results in a mosaic of microhabitats at a small spatial scale (Müller and Bütler, 2010), which may promote the establishment of new species in patches (Winter and Möller, 2008). In beech forests, the same phenomenon of increasing heterogeneity is expected, as it has already been shown that acoustic diversity is strongly correlated with LIDAR metrics (Pekin et al., 2012), and it is this type of plant diversity in vegetation structure and composition that has also been observed in our study sites by Menoni et al. (2006). In particular, the height and cover of field layers, horizontal and vertical diversity, and the level of complexity were significantly higher 10 years after restoration was completed (Menoni et al., 2006). Conversely, restoration in beech forests did not promote a significant increase in habitat heterogeneity. This is surprising because it is well established that the felling of trees generates new microhabitats by generating dead wood biomass resources (Müller and Bütler, 2010), altering the under canopy (Brakenhielm and Liu, 1998; Bos et al., 2007) and allowing for better light diffusion for understory plant species (Chazdon and Pearcy, 1991). Similar patterns were observed by Eldridge (Eldridge et al., 2018) who demonstrated that vocalizing avian species richness does not increase with habitat status. The restoration habitat in our study was subject to only one disturbance event over the course of a decade of regrowth, and potentially did not provide a range of niche spaces necessary for a diversity of avian species (Reid et al., 2012). It is possible that the resulting effects on biodiversity were too small to be measured by our spatial and temporal sampling. Daily and seasonal variations in acoustic biodiversity as a consequence of increase vocal activity or community structure Seasonal variations in acoustic biodiversity can be explained in two ways: i) the acoustic activity of animals varies with their annual biological rhythm, and ii) species community composition and individual densities may over the season. Most animal sound signals are emitted in the context of breeding, which is distinctly seasonal in temperate habitats. In songbirds, peak singing activity occurs around March-April (Perrins, 2008; Thompson et al., 2017). This may explain the decrease in dH observed in the restored beech forest early in the season (i.e. late April – early May). Besides, insect song activity is likely to increase with temperature and may explain the increase in dH observed in pine forests early in the recording season (late May – early June: Archer and Elgar, 2003). Another hypothesis explaining seasonal variations in dH could be due to fluctuations in animal population size (Coulson et al., 1997) and/or spatial distribution in the habitat (Rubenstein and Hobson, 2004). Daily variations in acoustic biodiversity can be explained by circadian variations in the singing activity of birds and insects. Most birds are more vocal early in the day (Stanley et al., 2016; Da Silva and Kempenaers, 2017), while insects must wait until the warmest hours to become fully active (Gogala and Riede, 1995; Thompson et al., 2017). All monitored environments show a marked peak in dH before and around sunrise, underlying the bird dawn chorus (Stanley et al., 2016; Farina and Ceraulo, 2017; Da Silva and Kempenaers, 2017). Of note, we found no significant difference between control and restored habitats in the morning regardeless of habitat type. Either bird diversity and numbers were not impacted by restoration, or our monitoring periods (beginning in late April or late May for beech and pine forests, respectively) were not optimal for capturing differences in bird vocal activity. Conversely, dH measured in the afternoon differed strongly between control and restored situations in both beech and pine forests. This highlights that insect diversity increased following restoration (Sueur et al., 2008a). Advantages and disadvantages of acoustic biodiversity assessment and the noise issue While the contours of ecoacoustics have been previously reviewed, including key theories, concepts, methodological processes, and outcomes that can be expected (Sueur and Farina, 2015), our study highlights that the acoustic monitoring of biodiversity based on index calculations provides useful data that can inform conservation policies. This long-term approach provides a unique opportunity to measure the temporal dynamics of acoustic biodiversity at different time scales, from daily to seasonal variations. It appears to be sufficiently sensitive to highlight variations in either biodiversity, animal behaviours, or both between forest habitats that share a common origin and have experienced different management procedures. It is possible to monitor seasonal and daily variations and to focus on specific taxa (birds and insects). However, the bioacoustics approach has several drawbacks. First, it remains difficult to determine whether the variation in acoustic complexity is due to more species or more individuals or differing behaviours of the same species. Second, the dH and D indices only capture elements of biodiversity and its variation. One reason is that they apply to acoustic signals that were recorded over a limited frequency band (e.g., the absence of ultrasound percludes assessment of bat diversity, wheres bats are good indicators of environmental quality: De Conno et al., 2018), and over a limited time period. Another important aspect is the noise constraint (Obrist et al., 2010; Sueur et al., 2012). Noise disturbs acoustic recordings by masking the time, frequency and amplitude features of biological signals (Vaseghi, 2001; Obrist et al., 2010). In a natural environment, the two main sources of noise are anthropogenic noises, i.e. anthropophony, caused by transportation traffic and any other human activity (Buxton et al., 2018b) and abiotic noise emerging from various natural phenomena, i.e. geophony, such as rain, streams, etc (Brumm and Slabbekoorn, 2005; Laiolo, 2010). Since anthropogenic noise is generally characterized by low frequencies, conventional high-pass filters remove them very efficiently (Stoddard, 1998). Conversely, abiotic noises are often broadband frequencies, which alter the overall frequency spectrum of the recorded soundscape. These noises can seriously bias the calculation of acoustic indices as they can differ significantly between recording situations (Sueur et al., 2014). One of the main challenges in soundscape analysis is the consideration and treatment of these primary sources of noise. This paper highlights the importance of the analysis step, in that it is necessary to quantify a reference noise per day and per recording station. This should be considered as a reference recording when calculating the indices. In conclusion, this study successfully measured the biodiversity response to habitat restoration in beech and mountain pine forests, demonstrating that a long-term ecoacoutics approach can quantify the benefits of environmental restoration. The temporal sampling provided by these new ecoacoustic tools thus offers a valuable means to monitor the effectiveness of forest restoration, as has been done in other environments or situations, such as soil restoration projects (Robinson et al., 2023) or post-wildfire recovery (Spatharis et al., 2024) . However, its usefulness is not limited to these applications, as ecoacoustics is also increasingly used in an active manner, leveraging soundscapes to benchmark and fast-track the recovery of ecological communities (Znidersic and Watson, 2022). This paper thus provides real insight into the effects of restoration work and offers clear links between ecology and forest management (Sayer et al., 2004). Although we point out the background noise issue, we emphasize that such an approach allows for the collection of biodiversity data over a large spatial and temporal scale with a high and consistent repetition rate. This is essential for documenting the restoration of habitat quality to its previous state. This restoration work has allowed the western capercaillie to return and successfully breed in a beech forest habitat that had been abandoned by the species for over 20 years. The mosaics created within rhododendrons appeared to be attractive to birds as forest managers noted the presence of birds with GPS tracking equipment (Ménoni et al., 2018). Several hundred hectares of habitat that have been subject to similar interventions are widely distributed in the Pyrenees. Two European projects (Gallipyr and habios) have funded this work in both France and Spain, on a scale that goes beyond the purely experimental stage (Ménoni, 2013). 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For scale, note that site 5 is in another valley 15 km from site 2. (b) Daily sampling from M1 to M8 (morning) and from A1 to A8 (afternoon) based on sunrise time. The M5 sampling period corresponds to sunrise. Sampling lasts 150 s every 15 1050 s (15 min). The M1 sequence starts 1 h before sunrise and the A1 sequence starts 6 h after sunrise. (c) Temporal sampling between habitat types. We made raw recordings from April 21th to June 12th, 2017 (54 days) for each beech forest site (sites 1 and 2), from May 22th to July 12th, 2017 (52 days) for each pine forest site (sites 3 and 4), and from April 22th to May 20th, 2017 (29 days) for the mixed forest (site 5). Figure 2. Acoustic entropy difference dH between signals and residual background noise as a function of habitat and restoration context (a,b,c) and intrasite vs intersite acoustic dissimilarity D (d, e). For dH, the level of acoustic diversity and entropy is higher in restored habitats compared to control sites but does not reach that observed in an unmanaged forest (mixed forest at site 5). LMER are based on 30 recording days for beech forests, 28 recording days for mountain pine forests and 11 recording days for beech forests - mixed forest comparison. For D, spatial heterogeneity of acoustic biodiversity is higher in the restored mountain pine forest compared to control sites. LMER are based on 30 recording days for beech forests and 28 recording days for mountain pine forests, with one average measurement per day. Significance of site fixed effects in the models, determined by anova tests, is represented by stars. Where appropriate, significant differences from Tukey post-hoc tests are represented by stars. NS: non significant, (*): p-value Figure 3. Seasonal change in acoustic entropy difference dH for beech forests (a) and mountain pine forests (b). The dH index decreases in the restored beech forest at the beginning of the recording season (i.e. late April – early May). The dH index increases in the pine forest at the beginning of the recording season (late May – early June). LMER tests are based on 30 recording days for beech forests and 28 recording days for mountain pine forests, with 48 measures per day. The significance of date fixed effects in the models, determined by anova tests, is represented by stars (***: p-value <0.001). Figure 4. Daily evolution of acoustic entropy difference (dH) for beech forests (a) and mountain pine forests (b). All monitored environments show a marked peak in dH before and around sunrise. LMER tests are based on 30 recording days for beech forests with 90 measures for each sequence from M1 to A8 and 28 recording days for mountain pine forests with 84 measures for each sequence from M1 to A8. Significance of daily time fixed effects in the models, determined by anova tests, is represented by stars (***: p-value <0.001). Figure 5. Difference in acoustic entropy (H) as a function of daily recording times and sites. The dH index differs strongly between control and restored conditions, with higher dH at restored sites compared to control sites. LMER tests are based on 30 recording days for beech forests with 720 measurements for each daily period, and 28 recording days for mountain pine forests with 672 measurements for each daily period. Significance of daily period fixed effects in the models, determined by anova tests, is represented by stars. NS: non significant, (*): p-value <0.05, (***): p-value <0.001). Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Tables Table 1. Temporal sampling of acoustic recordings. “Raw recordings” correspond to the total number of recordings for the entire monitoring period; “Remaining recordings” were selected by eliminating recordings affected by adverse weather conditions; “Analyzed recordings” correspond to the recordings of the sub-sample M1 to A8 sequences. Site Habitat Elevation Date of raw recording Raw recordings Remaining recordings Analyzed recordings Days Hours Days Hours Days Hours 1 – 2 Beech forest 1: 1495 ± 5 m 2: 1538 ± 13 m April 21 st to June 12 th 54 648 30 360 30 60 3 – 4 Mountain pine forest 3: 1871 ± 4 m 4: 1897 ± 4 m May 22 nd to July 12 th 52 624 28 336 28 56 5 Mixed forest 5: 1502 ± 6 m April 22 nd to May 20 th 30 360 11 132 11 22 Table 2. Linear Mixed Effects Models (LMER) explaining dH and D variabilities. We present the dH and D comparison across sites (a), the dH comparison across daily period (b) and across the season (c).For dH, the site effect was tested on slope and intercept separately. When several models were possible, the best model was evaluated using Akaike Index Criterion (AIC). Selected models are presented in bold type. Concerning the meaning of random effect formulas of LMER. If the name of grouping factor is denoted g, and x and y represents the explanatory variable and the explained variable, respectively; (1|g): different g have different intercepts, the slope of y over x is the same for all g; (0+x|g): the intercept is the same for all g, the slope of y over x varies among g; (1+x|g): different g have different intercepts, and the slope of y over x varies among g. LMER to assess the site effect on dH and D Syntax Response variable Model Beech forest Site 1 and 2 Mountain pine forest Site 3 and 4 Beech forest – Mixed forest Site 5 1 dH Site+(1|Recorder) -1111.9 -967.3 -535.4 2 dH Site+(0+Date|Recorder) -964.2 -1062.6 -485.7 3 dH Site+(1+Date|Recorder) -1120.0 -1059.7 -587.8 4 D Site+(1|Date) -486.6 -401.8 - LMER to assess the daily time effect on dH Syntax Response variable Model Beech forest Site 1 and 2 Mountain pine forest Site 3 and 4 5 dH Period+(1|Recorder) -12265.3 -12884.5 (c) LMER to assess the seasonal effect on dH Syntax Response variable Model Beech forest Mountain pine forest Control Site 1 Restored Site 2 Control Site 3 Restored Site 4 6 dH Date+Period+Hour+(1|Recorder) -5816.6 -9301.9 -6435.9 -6533.3 7 dH Date*Period*Hour+(1|Recorder) -5183.3 -8797.4 -6660.5 -5946.6 Supplementary methods Study site Sites 1 and 2 were situated in beech forests; sites 3 and 4 were situated in mountain pine forests and a 5 th site was located within a mixed forest outside of the Orlu Reserve, in another valley 15 km from site 2. Supplementary figure S1: Global map of the site and spatial sampling in Ariège, France. Filtering processes Method We applied a spectrum filter to reduce the impact of natural background noise. Here we illustrate the effects of this filter on recordings. For that, we selected a M4 sequence of 150 s and a sequence of 150 s including only background noise from the April 21 th , 2017 recordings of the control beech forest. We applied a band-pass filter between 200 Hz and 24000 Hz on these two sequences. We computed the mean spectrum of the background noise (b), which when removed from the M4 sequence (a) gives the signal (c). We also removed the mean background spectrum from the background noise itself to obtain the residual background noise (d). Results Supplementary figure 2S: Spectrum filter. The filter effectively removes the background noise from the M4 sequence (mainly located at low frequency). Spectra are presented at the scale of maximum amplitude. The spectrum filter effectively removes the background noise from M1 to M8 raw sequences to obtain signals sequences significantly less noisy. However, the filter applied to the background noise itself reveals that it only removes a part of background noise, leaving a residual background noise in signal sequences. Acoustic biodiversity indices reliability with bird choruses Method Acoustic indices have previously been tested in temperate forests, however, they have not been used in beech and mountain pine forests. Here, we performed tests of acoustic indices H and D on artificial choruses solely composed of known birds of the studied forests (i.e. inventoried by human counts). The aim was to determine the theoretical efficiency of acoustic indices in order to measure the acoustic diversity of species which may be singing in studied habitats. Acoustic signals of 34 birds were recovered and modified in order to constitute artificial specific signals of 150 s duration (Audacity Team, 2016, sampling freq. = 22.1 kHz). To form choruses, recordings were successively and randomly added, starting with a series of sound files of a single species to a series of sound files with sixteen distinct species. This procedure was repeated 100 times for each series leading to 1600 sound files (e.i. 16 series * 100 repetitions = 1600 choruses). H was calculated on each of these 1600 choruses and averaged for each series. To form chorus pairs, recordings were then successively and randomly added in two groups, starting with a series of sound files pairs with a single unshared species to a series of sound files pairs with height unshared species. This procedure was repeated 100 times for each series leading to 800 sound files pairs (8 series * 100 repetitions = 800 chorus pairs). D was calculated on each of these 800 chorus pairs and averaged for each series. Results Supplementary Figure 3S: Acoustic entropy (H) depending to the number of bird species per artificial choruses. N = 100 different choruses per condition. Significant differences of post-hoc tests (Tukey) are represented by letters. Supplementary Figure 4S. Acoustic dissimilarity (D) depending on the number of unshared bird species per pairs of artificial choruses. N = 100 different pairs of choruses per condition. Significant differences of post-hoc tests (Tukey) are represented by letters. Linear models showed an increasing H with the increase of the bird species number in the choruses and a linear increasing D index with the increase of the unshared bird species number between chorus pairs. These indices are still well representative of the species richness above 15 species of birds, with much less saturation for D. Our results prove that the acoustic indices capacity to measure the acoustic diversity variation of local bird diversity. Consideration of residual background noise Method In field acoustic surveys, processes are used to reduce impacts of the background noise on acoustic indices (e.g. filters). However, previous studies never considered the relative part of the background noise which was not removed through filtering processes (here named residual background noise) for the calculation of the acoustic indices. Here, H was separately calculated on sequences of “signals” and “residual background noises” to really measure their relative contribution on the calculation of acoustic diversity. Results Acoustic entropy (H) Comparisons N T P-value Site 1 (RN) – Site 2 (RN) 30 -5.33 1.03*10 -5 Site 1 (S) – Site 2 (S) -15.1 2.80*10 -15 Site 1 (RN) – Site 1 (S) -0.990 3.30*10-1 Site 2 (RN) – Site 2 (S) -11.7 1.81*10 -12 Site 3 (RN) – Site 4 (RN) 28 27.5 2.20*10-16 Site 3 (S) – Site 4 (S) -3.19 3.63*10 -3 Site 3 (RN) – Site 3 (S) -0.764 4.51*10-1 Site 4 (RN) – Site 4 (S) -19.7 2.20*10-16 Site 1 (RN) – Site 2 (RN) 11 -1.28 2.30*10-1 Site 1 (S) – Site 2 (S) -6.80 4.77*10 -5 Site 1 (RN) – Site 1 (S) 1.39 1.94*10 -1 Site 2 (RN) – Site 2 (S) -6.91 4.17*10-5 Site 1 (RN) – Site 5 (RN) 5.16 4.26*10-4 Site 1 (S) – Site 5 (S) -1.50 1.65*10-1 Site 5 (RN) – Site 5 (S) -23.7 4.11*10-10 Site 2 (RN) – Site 5 (RN) 8.01 1.17*10-5 Site 2 (S) – Site 5 (S) 3.43 6.40*10-3 Site 5 (RN) – Site 5 (S) -6.90 4.17*10-5 Supplementary Table 1S. A coustic entropy (H) calculated on signals and residual background noises sequences (paired T tests). “RN” is related to residual background noise; “S” to signals, “T” to the T-Value and “N” to the number of mean measures (i.e. for the three recorders of the same site and averaged per day). Supplementary Figure 5S: Acoustic entropy (H) depending on signals and residual background noises. The level of acoustic diversity and entropy is higher (higher H) for signals compared to noise in all restored sited of the three studied habitats. “Noise” corresponds to noise remaining after noise filtration and “Signal” corresponds to the addition of the residual background noise and animal acoustic signals. T-tests were realized between site 1 and site 2 (30 mean measures), between site 1, site 2 and site 5 (11 mean measures) and between site 3 and site 4 (28 mean measures); a mean measure corresponds to the mean H index from M1 to A8 signals sequences or for residual background noise sequences for a day and for the three recorders of the same site. Significant differences are represented by letters, independently of habitat type. H calculated on signals of control beech forest and control mountain pine forest were not different to H calculated on the noise. Whereas H calculated on signals of restored beech forest and restored mountain pine forest are higher than H calculated on the residual background noises of these respective sites. Moreover, mixed forest is characterized by the most significant complexity difference between signals and noise. However, when only considering signals, mixed forest could have been considered as the least acoustically diversified habitat. 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Keywords biodiversity indicators biodiversity response conservation ecoacoustic habitat restoration western capercaillie Authors Affiliations Thomas Betton UJM View all articles by this author Kévin Foulché Office francais de la biodiversite View all articles by this author Emmanuel Menoni retired from National Office of Biodiversity View all articles by this author Claude Novoa retired from National Office of Biodiversity View all articles by this author Florence Nicole UJM View all articles by this author Mark H VAN NIEKERK UJM View all articles by this author Nicolas Mathevon UJM View all articles by this author Frederic SEBE 0000-0003-0264-9519 [email protected] Office francais de la biodiversite View all articles by this author Metrics & Citations Metrics Article Usage 366 views 179 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Thomas Betton, Kévin Foulché, Emmanuel Menoni, et al. Acoustic Monitoring of Forest Restoration for the Western Capercaillie. Authorea . 31 March 2025. DOI: https://doi.org/10.22541/au.174341564.46675632/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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