Acoustic Evenness Index Research Articles

Unsupervised selection of acoustic indices: An experimental comparison for characterizing unlabeled audio recordings from sub-Andean forest soundscapes

We present an experimental comparison of applying six unsupervised (i.e. not relying on class labels) and almost parameterless feature selection methods for ranking acoustic indices. The study is aimed at guiding the practitioner in choosing appropriate acoustic indices when, in absence of class labels, a small but meaningful number of features to characterize soundscapes is desired. Forty acoustic indices were considered, which correspond to seventeen temporal, spectral and soundscape features, along with their basic statistics. Three publicly available soundscape datasets, registered in a sub-Andean forest, were used for the experiments; moreover, several subsets were considered according to the different times of the day. Results reveal that the Acoustic Evenness Index is the most important one in terms of representational power according to the six considered selection criteria, followed by the Acoustic Complexity Index when conditions are relaxed to examine features ranked among the top-five. Besides, the Bioacoustic Index, the Acoustic Diversity Index and the Root Mean Square Energy stand out as important features when characterizing days in their separated parts.

Virtual Reality Technology in Analysis of the Sarek National Park Soundscape in Sweden

The paper presents an in-depth analysis of the soundscape within Sarek National Park, the oldest national park in Europe, situated in Lapland, northern Sweden. The comprehensive acoustic measurements, ambisonic recordings, and 360X video recordings were carried out during a scientific expedition in the summer of 2020. The aim of the paper is to show the soundscape analysis of carefully selected characteristic locations in various parts of the valley. The paper extensively discusses the findings derived from the recorded data using both classical acoustic methods and the soundscape approach. The classic acoustic parameters, commonly employed in environmental acoustics as well as eco-acoustic indices such as: ACI (acoustic complexity index), ADI (acoustic diversity index), AEI (acoustic evenness index), NDSI (normalized difference soundscape index), BIO (energy level of biophony), amplitude index (M), and total entropy (H) were calculated. To gain further insights, listening tests, facilitated through virtual reality tools, were conducted, enabling participants to engage in soundwalk experiences. By employing a combination of traditional acoustic methods and innovative soundscape approaches, the paper presents a holistic evaluation of the auditory environment in Sarek National Park. The main contribution of the presented research is providing new data from the unique, geographically inaccessible region of the world, the Sarek National Park. This research not only enriches our understanding of the national park’s soundscape but also offers valuable insights into the interaction between the natural environment and human perception of sound.

Time and habitat structure shape insect acoustic activity in the Amazon.

Insects are the most diverse animal taxon on Earth and play a key role in ecosystem functioning. However, they are often neglected by ecological surveys owing to the difficulties involved in monitoring this small and hyper-diverse taxon. With technological advances in biomonitoring and analytical methods, these shortcomings may finally be addressed. Here, we performed passive acoustic monitoring at 141 sites (eight habitats) to investigate insect acoustic activity in the Viruá National Park, Brazil. We first describe the frequency range occupied by three soniferous insect groups (cicadas, crickets and katydids) to calculate the acoustic evenness index (AEI). Then, we assess how AEI varies spatially and temporally among habitat types, and finally we investigate the relationship between vegetation structure variables and AEI for each insect category. Overall, crickets occupied lower and narrower frequency bands than cicadas and katydids. AEI values varied among insect categories and across space and time. The highest acoustic activity occurred before sunrise and the lowest acoustic activity was recorded in pastures. Canopy cover was positively associated with cricket acoustic activity but not with katydids. Our findings contribute to a better understanding of the role of time, habitat and vegetation structure in shaping insect activity within diverse Amazonian ecosystems. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

The impact of vehicular noise on acoustic indices within simulated bird assemblage soundscapes

ABSTRACT Passive acoustic monitoring (PAM) is a sampling technique that has gained increasing popularity in the field of wildlife monitoring and research since it allows for non-invasive and cost-effective collection of acoustic information. Retrieving biological information from PAM recordings can often involve time-consuming sound annotation methodologies, but the advent of acoustic indices can help expedite this process. While correlations between acoustic indices and species richness have been observed in a variety of ecological contexts, these relationships faulter in environments with increased vehicular noise. Here, we assessed the direct impact of vehicular noise on nine acoustic indices through controlled manipulation of vehicular noise within computer-generated bird assemblage soundscapes. Our results demonstrate that recording distance from roadsides and number of passing cars per minute have notable and persistent impacts on acoustic index values, but the magnitude of the effect varies across indices. Four acoustic indices demonstrated greater resilience to vehicular noise interference and may therefore be better suited for developed areas: Bioacoustic Index, Acoustic Complexity Index, Acoustic Diversity Index, and Acoustic Evenness Index. By contributing to the collective understanding of acoustic index behaviours under anthropogenic noise pollution, we hope to better inform their ecological application in human-developed contexts.

Recognition of bird species with birdsong records using machine learning methods.

The recognition of bird species through the analysis of their vocalizations is a crucial aspect of wildlife conservation and biodiversity monitoring. In this study, the acoustic features of Certhia americana, Certhia brachydactyla, and Certhia familiaris were calculated including the Acoustic complexity index (ACI), Acoustic diversity index (ADI), Acoustic evenness index (AEI), Bioacoustic index (BI), Median of the amplitude envelop (MA), and Normalized Difference Soundscape Index (NDSI). Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were constructed. The results showed that the XGBoost model had the best performance among the three models, with the highest accuracy (0.8365) and the highest AUC (0.8871). This suggests that XGBoost is an effective tool for bird species recognition based on acoustic indices. The study provides a new approach to bird species recognition that utilizes sound data and acoustic characteristics.

Linking ecoacoustic indices to psychoacoustic perception of the urban acoustic environment

Research about human perception of the acoustic environment is dominated by the use of sound pressure levels (SPL) or proprietary psychoacoustic indicators, limiting the link between sound and perception to only these two approaches. The aim of this study is to make connections between soundscape perception descriptors and ecoacoustic indices so that a perceptual dimension of ecoacoustic indices is ascertained. The study employs a laboratory experiment where 309 participants were exposed to sound and imagery from nine different urban land uses (n = 2,781) according to the soundscape protocol DIN ISO 12913-2. Spearman’s correlation and ordinal logistic regression are used to analyze the relationship between continuous numeric ecoacoustic indices and Likert scale psychoacoustic perception outcomes. We find that the predictors median amplitude (M), acoustic richness (AR), number of peaks (NP), and acoustic evenness index (AEI) have significant correlations (r > 0.5: p ≤ 0.05) and effect sizes (β ≥ 0.1) for the psychoacoustic outcomes perceived affective quality (PAQ) of calm, pleasant, chaotic, annoying, and soundscape identifiers (SSI) traffic sounds and natural sounds. We find that between 59.4 % and 81.9 % of the variation within dependent SSI and PAQ variables is explained by ecoacoustic indices based on Nagelkerke’s R-Squared values. High M and AR values are indicators for sound environments with high median or consistent amplitude, such as traffic noise, and high NP and AEI values are indicators for high fidelity sound environments with natural sounds or human beings that may be perceived as calm or pleasant. We conclude that combinations of M, AR, NP, and AEI as composite indicators contribute to understanding nine of the 14 psychoacoustic perception outcomes in DIN ISO 12913-2.

The soundscape method and eco-acoustic indicators in the analysis of the acoustic environment of the mountain national parks in Poland and Sweden

Soundscape is one of the essential environmental elements affecting people’s experience in nature spaces. In the complex mechanism of landscape and soundscape perception, the acoustic characteristics of the environment are the primary influencing factor. The aim of the paper is the analysis of soundscape of two mountain national parks—Tatra National Park (TNP) in Poland and Sarek National Park (SNP) in Sweden. The first park is very frequently visited by tourists (around 4 million tourists a year) while the second park has an attendance of around a few thousand tourists. The paper presents the results of the analysis of acoustic measurements and ambisonic recordings made in the Koscieliska Valley (TNP) and the Rapa Valey (SNP, Swedish: Rapadalen) using classical method and the soundscape method. In addition, psychoacoustic parameters and soundecology indicators such as loudness, sharpness or roughness, ACI (acoustic complexity index), NDSI (normalized difference soundscape index), BIO (bioacoustic index), ADI (acoustic diversity index), and AEI (acoustic evenness index) were calculated.

Acoustic indices as proxies for bird species richness in an urban green space in Metro Manila.

We assessed eight acoustic indices as proxies for bird species richness in the National Science Complex (NSC), University of the Philippines Diliman. The acoustic indices were the normalized Acoustic Complexity Index (nACI), Acoustic Diversity Index (ADI), inverse Acoustic Evenness Index (1-AEI), Bioacoustic Index (BI), Acoustic Entropy Index (H), Temporal Entropy Index (Ht), Spectral Entropy Index (Hf), and Acoustic Richness Index (AR). Low-cost, automated sound recorders using a Raspberry Pi were placed in three sites at the NSC to continuously collect 5-min sound samples from July 2020 to January 2022. We selected 840 5-min sound samples, equivalent to 70 hours, through stratified sampling and pre-processed them before conducting acoustic index analysis on the raw and pre-processed data. We measured Spearman's correlation between each acoustic index and bird species richness obtained from manual spectrogram scanning and listening to recordings. We compared the correlation coefficients between the raw and pre-processed.wav files to assess the robustness of the indices using Fisher's z-transformation. Additionally, we used GLMMs to determine how acoustic indices predict bird species richness based on season and time of day. The Spearman's rank correlation and GLMM analysis showed significant, weak negative correlations between the nACI, 1-AEI, Ht, and AR with bird species richness. The weak correlations suggest that the performance of acoustic indices are dependent on various factors, such as the local noise conditions, bird species composition, season, and time of day. Thus, ground-truthing of the acoustic indices should be done before applying them in studies. Among the eight indices, the nACI was the best-performing index, performing consistently across sites and independently of season and time of day. We highlight the importance of pre-processing sound data from urban settings and other noisy environments before acoustic index analysis, as this strengthens the correlation between index values and bird species richness.

Investigating the effects of tree species diversity and relative density on bird species richness with acoustic indices

Mixed-species forest stands may provide suitable habitats for more bird species than tree monocultures by increasing the number of niches and resources available. However, assessing the effects of forest diversity on birds over time and across multiple sites using traditional bird survey methods can be expensive, time-consuming and impractical. Alternatively, we used passive acoustic monitoring (PAM) and acoustic indices to investigate how tree species diversity, relative density, and other plot characteristics affect the species richness of avian communities in temperate forests. Acoustic monitoring was conducted in Nationalpark Hainich, Germany, using plots which differed in tree species diversity and relative species density. Using a subset of our data, five acoustic indices, the normalised difference soundscape index (NDSI), acoustic complexity index (ACI), bioacoustic index (BI), the acoustic evenness index (AEI) and temporal entropy (Ht), were initially assessed using audio recordings of different soundscape elements to determine the degree to which they were influenced by non-biophony soundscape elements. Of these, NDSI, ACI and BI were considered least biased and displayed significant positive relationships with bird species richness assessed using audio recordings. However, NDSI values plateaued at around 5 bird species when measuring bird species richness and ACI values for dawn choruses were lower than expected, potentially due to the high density of birdsong saturating the soundscape. Plot tree species diversity had significant positive effects on ACI and BI during dawn, dusk and daytime periods. Increased basal area of sycamore (Acer pseudoplatanus) and, to a lesser degree, beech (Fagus sylvatica), had a negative effect on acoustic indices. These results could potentially be due to the earlier leaf out timings of these tree species in relation to the dates when surveys were performed. While the use of PAM and acoustic indices enabled surveys across multiple sites over longer time periods than would previously have been achievable, our results show that care should be taken when selecting indices and interpreting acoustic index results.

Free-ranging livestock changes the acoustic properties of summer soundscapes in a Northeast Asian temperate forest

If not properly managed, free-range livestock grazing could be a great threat to forest ecosystems by causing heterogeneous and unpredictable changes in forest structure, functions, and biodiversity. Understanding these changes is important for informing land management decisions compatible with long-term biodiversity conservation. Passive acoustic monitoring (PAM) of biodiversity may provide integrative indices for assessing grazing effects. However, little is known about the responses of soundscape characteristics to forest grazing, especially in temperate forests. Here, we use bioacoustics to assess the impact of continuous livestock grazing on acoustic indices at the landscape scale in Northeast China. We compared the acoustic complex index (ACI), acoustic diversity index (ADI), acoustic evenness index (AEI), bioacoustic index (BIO), total acoustic entropy (H), and normalized difference soundscape index (NDSI) between 16 cattle-grazed forest plots and 36 ungrazed forest plots for different time scales (24 h diel cycles, dawn, day, dusk and night). We collected and analysed over 7000 h of recordings from 52 sites. Our results showed that the six acoustic indices exhibited distinct diel patterns in both forest habitats. Moreover, several indices, except for the ADI and AEI, significantly responded to grazing over 24 h diel cycles, but with contrasting patterns; compared to grazed forests, ungrazed forests had much more bioacoustics activity measured by significantly higher ACI and NDSI values and significantly lower BIO and H values. Livestock grazing was a key driver of diel acoustic patterns, resulting in the loss of nocturnal and dawn soundscapes. Our results indicate that intensive/excessive livestock grazing has a significant negative impact on soundscape quality in human-dominated temperate forest areas, which is likely triggered by shifts in faunal assemblages. Livestock, for example, may reduce the occupancy of sika deer (Cervus nippon), resulting in fewer sika deer vocalizations in grazed forests. Cattle lowing and the intensive sounds of cattle bells may also mask biological vocalizations. We argue that multiple acoustic indices are required to adequately account for the complex responses of soundscapes to large-scale livestock grazing. For forest management, we propose employing PAM to quantify the acoustic communities of the landscape from a new perspective, thereby fostering evidence-based forest management at the local scale to reconcile the livestock sector with ecosystem conservation.

Quantifying the Soundscape: How filters change acoustic indices

Monitoring biodiversity can be time consuming and costly. Automated recording units (ARUs) have rapidly emerged as an efficient and cost-effective tool for measuring biodiversity. Acoustic indices are one output from recordings from ARUs that can be quantified to serve as an ecological indicator for biodiversity. However, there is a lack of guidance on what acoustic filters to apply to these indices and when. To address this gap, we collected acoustic data from study locations spanning temperate and tropical forests, agricultural grasslands and croplands, and peri-urban development. We applied filters of 80, 500, 1000, and 2000 Hz to these data when calculating the different indices. In addition, we considered the effect landscape context, road noise, season, and elevation have on seven of the most commonly used acoustic indices with different frequency filters. We found that two indices, Acoustic Diversity Index (ADI) and Acoustic Evenness Index (AEI), were most sensitive to filtering, changing significantly between an 80 and 1000 Hz filter across the different covariates. Acoustic Complexity Index (ACI), however, remained consistent with the different filters. These results suggest that when using acoustic indices, one should be cognizant of the context of the study location and the season of the study period when using ADI and AEI. ACI can be used more generously since it is not as sensitive to filtering. ARUs and acoustic indices are an effective tool for measuring biodiversity, but to ensure proper reporting and ability to compare results across studies, more guidelines on appropriate filtering of acoustic indices should be developed.

A Soundscape Assessment of the Oak Forests in the National Park “Homilshanski Lisy” (Northeastern Ukraine)

Abstract We investigated the properties of the sounds recorded on the territory of the National Park “Homilshanski Lisy” (Kharkiv region, Ukraine). Recordings were made at five points (in mature, middle-aged, and young oak forests, overgrown clear-cut and aspen forests). Data collection was carried out using on-site positioning of AudioMoth autonomous recorders, located on trees at a height of 1.5 m. The recording was made from April 11 to July 10, 2020, for 3 h in the morning and evening with a 5-min duration followed by a 10-min pause (24 recordings per day). Six acoustic indices (AIs) were calculated: Acoustic complexity index (ACI), acoustic diversity index (ADI), acoustic evenness index (AEI), bioacoustic index (BI), normalized difference soundscape index (NDSI), and acoustic entropy index (H). For the analysis, we used the Friedman test as well as a nonparametric analysis of the variance of the distance matrix and Tukey’s test. The results of the analysis showed the statistical significance of the influence of forest type, date and time of recording, as well as the effect of their pairwise interactions on all six acoustic indices, both in the morning and evening. For three indices – ACI, BI, and NDSI – the highest average values were noted in a mature oak forest and the lowest was in overgrown clear-cuts. We performed a PCA to reduce the number of variables and obtain insight into the variable relevance. The cumulative percentage of variance, explained by the first three principal components, is 84.5%. The first principal component is associated with H, BI, AEI, and ADI. The second and third principal components are associated with NDSI and ACI. The obtained results correspond to the results of quantitative bird counts carried out earlier in this area.

Biodiversity assessment using passive acoustic recordings from off-reef location—Unsupervised learning to classify fish vocalization

We present the quantitative characterization of Grande Island's off-reef acoustic environment within the Zuari estuary during the pre-monsoon period. Passive acoustic recordings reveal prominent fish choruses. Detailed characteristics of the call employing oscillograms and individual fish call parameters of the segmented data include vocal groups such as Sciaenidae, Terapon theraps, and planktivorous as well as invertebrate sounds, e.g., snapping shrimp. We calculated biodiversity parameters (i) Acoustic Evenness Index (AEI), (ii) Acoustic Complexity Index (ACI), and mean sound pressure level (SPLrms) for three frequency bands such as full band (50-22 050 Hz), the low-frequency fish band (100-2000 Hz), and the high-frequency shrimp band (2000-20 000 Hz). Here, ACI and AEI metrics characterize the location's soundscape data effectively indicating increased biodiversity of fish species for both the low-frequency and high-frequency bands. Whereas variations for SPLrms are prominent for three frequency bands. Moreover, we employ unsupervised classification through a hybrid technique comprising principal component analysis (PCA) and K-means clustering for data features of four fish sound types. Employed PCA for dimensionality reduction and related K-means clustering successfully provides 96.20%, 76.81%, 100.00%, and 86.36% classification during the dominant fish chorus. Overall, classification performance (89.84%) is helpful in the real-time monitoring of the fish stocks in the ecosystem.

Biotic sound SNR influence analysis on acoustic indices

In recent years, passive acoustic monitoring (PAM) has become increasingly popular. Many acoustic indices (AIs) have been proposed for rapid biodiversity assessment (RBA), however, most acoustic indices have been reported to be susceptible to abiotic sounds such as wind or rain noise when biotic sound is masked, which greatly limits the application of these acoustic indices. In this work, in order to take an insight into the influence mechanism of signal-to-noise ratio (SNR) on acoustic indices, four most commonly used acoustic indices, i.e., the bioacoustic index (BIO), the acoustic diversity index (ADI), the acoustic evenness index (AEI), and the acoustic complexity index (ACI), were investigated using controlled computational experiments with field recordings collected in a suburban park in Xuzhou, China, in which bird vocalizations were employed as typical biotic sounds. In the experiments, different signal-to-noise ratio conditions were obtained by varying biotic sound intensities while keeping the background noise fixed. Experimental results showed that three indices (acoustic diversity index, acoustic complexity index, and bioacoustic index) decreased while the trend of acoustic evenness index was in the opposite direction as signal-to-noise ratio declined, which was owing to several factors summarized as follows. Firstly, as for acoustic diversity index and acoustic evenness index, the peak value in the spectrogram will no longer correspond to the biotic sounds of interest when signal-to-noise ratio decreases to a certain extent, leading to erroneous results of the proportion of sound occurring in each frequency band. Secondly, in bioacoustic index calculation, the accumulation of the difference between the sound level within each frequency band and the minimum sound level will drop dramatically with reduced biotic sound intensities. Finally, the acoustic complexity index calculation result relies on the ratio between total differences among all adjacent frames and the total sum of all frames within each temporal step and frequency bin in the spectrogram. With signal-to-noise ratio decreasing, the biotic components contribution in both the total differences and the total sum presents a complex impact on the final acoustic complexity index value. This work is helpful to more comprehensively interpret the values of the above acoustic indices in a real-world environment and promote the applications of passive acoustic monitoring in rapid biodiversity assessment.

Island biogeography of soundscapes: Island area shapes spatial patterns of avian acoustic diversity

AbstractAimThe equilibrium theory of island biogeography predicts the positive species–area relationship and the negative species–isolation relationship, resulting in higher species richness on large and close islands. Unlike species richness, soundscape diversity integrates sound from various sources (e.g. biophony, geophony and anthrophony). However, how soundscape diversity varies with island area and isolation still needs to be tested. Here, we explored the island biogeography of bird soundscapes and the determinants of island attributes in shaping bird diversity and soundscape diversity.LocationThousand Island Lake, Zhejiang, China.TaxonBirds.MethodsWe recorded avian soundscapes by audio recorders and censused bird diversity by line transects on 20 land‐bridge islands. We calculated four acoustic indices (acoustic complexity index, bioacoustic index, acoustic evenness index and acoustic entropy index) to assess acoustic richness, evenness and heterogeneity to explore the soundscape diversity of birds. We used multiple linear regressions, spatial autoregressions and piecewise structural equation models to examine the relationships between bird richness and acoustic diversity, and island attributes.ResultsWe found positive diversity–area relationships for avian soundscapes. Larger islands had more vocal species and higher habitat diversity, which led to an increment in the richness and unevenness of avian soundscapes on large islands. Acoustic evenness decreased with increasing isolation (distance to the mainland).Main ConclusionsSoundscapes on large islands are more diverse than those on small islands. Rich acoustic assemblages and heterogeneous habitats promote increased soundscape diversity on islands. Conversely, the lack of vocal contributors, resulting in a decrement in the communication of acoustic signals, can create a lower soundscape diversity on small and remote islands. Our study emphasizes the necessity of examining both species and habitat diversity in island biogeography for better understanding the underlying mechanisms determining biological soundscapes on islands.

Mapping of the Acoustic Environment at an Urban Park in the City Area of Milan, Italy, Using Very Low-Cost Sensors

The-growing influence of urbanisation on green areas can greatly benefit from passive acoustic monitoring (PAM) across spatiotemporal continua to provide biodiversity estimation and useful information for conservation planning and development decisions. The capability of eco-acoustic indices to capture different sound features has been harnessed to identify areas within the Parco Nord of Milan, Italy, characterised by different degrees of anthropic disturbance and biophonic activity. For this purpose, we used a network of very low-cost sensors distributed over an area of approximately 20 hectares to highlight areas with different acoustic properties. The audio files analysed in this study were recorded at 16 sites on four sessions during the period 25–29 May (2015), from 06:30 a.m. to 10:00 a.m. Seven eco-acoustic indices, namely Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-Acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI), and Dynamic Spectral Centroid (DSC) were computed at 1 s integration time and the resulting time series were described by seven statistical descriptors. A dimensionality reduction of the indices carrying similar sound information was obtained by performing principal component analysis (PCA). Over the retained dimensions, describing a large (∼80%) variance of the original variables, a cluster analysis allowed discriminating among sites characterized by different combination of eco-acoustic indices (dimensions). The results show that the obtained groups are well correlated with the results of an aural survey aimed at determining the sound components at the sixteen sites (biophonies, technophonies, and geophonies). This outcome highlights the capability of this analysis of discriminating sites with different environmental sounds, thus allowing to create a map of the acoustic environment over an extended area.

Auto-correlations and long time memory of environment sound: The case of an Urban Park in the city of Milan (Italy)

We investigate the possible presence of ‘long time’ memory in the auto-correlations of biophonic activity of environment sound. The study is based on recordings taken at two sites located in the Parco Nord of Milan (Italy), characterized by a wooded land, rich in biodiversity and exposed to different sources and degrees of anthropogenic disturbances. The audio files correspond to a three-day recording campaign (1-min recording followed by 5-min pause), from (17:00) April 30 to (17:00) May 3, 2019, which have been transformed into ecoacoustic indices time series. The following eight indices have been computed: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-acoustic Index (BI), Acoustic Entropy Index (H), Acoustic Richness index (AR), Normalized Difference Soundscape Index (NSDI) and Dynamic Spectral Centroid (DSC). We have grouped the indices carrying similar sound information by performing a principal component analysis (PCA). This allows us to reduce the number of variables from eight to three by retaining a large (≳80%) variance of the original variables. The time series corresponding to the reduced set of new variables have been analyzed, and both seasonal and possible long term trend components have been extracted. We find that no trends are present, i.e. the resulting time series are stationary, and the auto-correlations of the three selected PCA dimensions and associated residuals (obtained after extracting the seasonal components) can be determined. The calculations reveal the presence of a “memory” of few (≲5) hours long in the environment sound, for the two sites considered, which is quantified by the Hurst exponent, H. For Site1, we find an overall effective Hurst exponent, Hdim≃0.88, for all three dimensions, and Hres≃0.75 for the residuals. For Site2, the exponents are slightly smaller, amounting to 0.80 and 0.60, respectively. We attempt to correlate the Hurst exponents with a quality index obtained from an aural survey, aimed at determining the sound components, such as biophonies, technophonies and geophonies, at the two sites. We conclude that the higher the Hurst exponents, the higher are the periodic-structured sounds, corresponding to stronger long-term biophonic activity. We find that Site1 has a more structured environment sound than Site2, also consistent with the major presence of tall trees surrounding the location of the acoustic sensor at the former.

Eco-Acoustic Assessment of an Urban Park by Statistical Analysis

We investigated the statistical properties of the sounds recorded at a site located in the Parco Nord of Milan, Italy, characterized by wooded land rich in biodiversity and exposed to different sources and types of anthropogenic disturbances with the aim of deriving information on its environmental quality in terms of biophonic presence and athropic disturbance. A time series of eco-acosutic indices were determined for 616 audio files recorded from 30 April 2019 (5:00 p.m.) to 3 May 2019 (5:00 a.m.) with a 1-min duration followed by a 5-min pause (10 recordings per hour). In the present study, the following indices were computed: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI) and Dynamic Spectral Centroid (DSC). Cluster analysis performed on the corresponding time series yielded a dimensional reduction from seven down to three. The results show a clear separation of the eco-acoustic indices into two clusters, reflecting the different dynamics and diversity behaviour throughout the recordings. A post-processing aural survey was also performed, aiming at determining biophonic activities (mainly avian vocalization and other animals), the characteristics of technophonies sources (mainly road traffic noise and airplane fly-overs), human presence (voices and steps) and geophonies (rain and wind). The statistical analysis proved to be a robust tool due to the good matching obtained with the aural survey outcomes. The overall quality of the Parco Nord phonic activity was found to be low. Notwithstanding the presence of avian species, highlighted by the characteristic dawn chorus, both clusters revealed low “scores” of NDSI and DSC indices heavily influenced by road traffic sources. This study represents the first step toward the realization of maps of eco-acoustic indices for the long-term monitoring of fragile habitats.

Annual acoustic dynamics are associated with seasonality in a monsoon tropical forest in South Vietnam

We studied the annual dynamics of the biotic component of the acoustic environment in a lowland monsoon tropical forest. The study was conducted in Nam Cat Tien National Park (southern Vietnam) from January 2013 to January 2014. We placed an autonomous recorder at the Nam Cat Tien ecosystem flux observation station at a height of 35 m. For the analysis, we selected 1–4 5-min recordings per day collected at 5.30 a.m., 12.00 a.m., 4.00 p.m., and 8.00 p.m. For each recording, we calculated four acoustic indices: the acoustic complexity index (ACI), the bioacoustic index (BIO), the acoustic diversity index (ADI) and the acoustic evenness index (AEI). These indices had roughly similar annual dynamics and peaked from May to August (i.e., immediately before the wet season and in the wet season). We then related the four acoustic indices to the daily values of the following meteorological variables: air temperature, air relative humidity, solar radiation, and precipitation sum. The BIO, ADI and AEI were most influenced by daily average humidity. We therefore concluded that humidity rather than precipitation, temperature or radiation was the best predictor of the annual dynamics of acoustic activity in Nam Cat Tien. The increase in humidity was apparent several months before the major rains began in 2013 (i.e., before the wet season). We suggest that future ecoacoustics studies in the tropics should give greater consideration to humidity as a variable determining the acoustic activity of local fauna.

Listening to a changing landscape: Acoustic indices reflect bird species richness and plot-scale vegetation structure across different land-use types in north-eastern Madagascar

New technologies like ecoacoustic surveys promise time and cost efficiency for biodiversity assessments, serve as a basis for effective conservation policies, and are particularly appealing for remote and highly diverse tropical areas. Acoustic indices facilitate the analysis of large acoustic datasets but no consensus on their performance has been reached yet. We evaluated the efficacy of four acoustic indices (Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy) for sound data analysis and biodiversity assessments inside a national park and the agricultural mosaic landscape of north-eastern Madagascar, a global biodiversity hotspot. We used self-built sound recorders to continuously record soundscapes on 80plots across seven land-use types (old-growth forest, forest fragment, forest–derived and fallow-derived vanilla agroforest, herbaceous and woody fallow, rice paddy) and compared index values between land–use types, assessed the correlation with bird species richness as measured by point counts, and related the acoustic indices to plot- and landscape-scale parameters. The Acoustic Diversity Index, Acoustic Evenness Index (inverse) and Acoustic Entropy were highest in old-growth forest and lowest for rice paddies and fallow land. Index values for structurally similar land-use types did not differ significantly from each other. The correlation of the three acoustic indices with bird species richness was strongest during daytime (R2≥0.30). Differences in the index values were best explained by land-use type and vegetation density. Our results showed that all investigated indices except the Acoustic Complexity Index were suitable biodiversity indicators for a tropical, agricultural landscape. Soundscape diversity was positively affected by plot-scale vegetation structure, emphasizing the importance of forests and particularly old-growth forest for conservation. We demonstrated that acoustic indices and sound recordings are a useful tool for assessing biodiversity in tropical agricultural mosaic landscapes. To realize the full potential of ecoacoustics in conservation, sampling guidelines and user-friendly analysis packages will be key to facilitate a wider implementation.