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

hardRain: An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data

hardRain:An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data

Evaluating forest restoration effects on timing of avian dawn chorus in Ranomafana National Park, Madagascar

Monitoring of forest restoration efforts is essential to ensure healthy, self-sustaining tropical rainforests. Passive acoustic monitoring is used to monitor vocal activity of birds, which play a key role in forest ecosystems as seed dispersers. Communication between birds seems most profitable during a peak of bird singing in the morning, known as the dawn chorus. Anthropogenic disturbances leading to increased light levels affect the timing of this chorus in individual species. This research sheds a light on the effect of forest restoration on the dawn chorus using automatic detection methods to identify bird sounds from acoustic data. Machine learning methods like clustering and pattern matching were used alongside a manual analysis to describe the dawn chorus in protected forests as well as restoration sites around Ranomafana National Park, Madagascar. Restoration sites were found to have lower species richness and increased interference from insect sounds. No difference was found between timing of the dawn chorus in both forest habitats. This can possibly be assigned to changes in community composition and decreased detectability of species in insect-dominated landscapes. Future research could further disentangle these effects, by filtering of acoustic data, development of workflow pathways and the use of stronger machine learning methods that allow for more reliable species-specific detection. In the current state of automatic acoustic methods, close cooperation with local experts is recommended to achieve effective monitoring in tropical rainforests

A framework for quantifying soundscape diversity using Hill numbers

Soundscape studies are increasingly used to capture landscape-scale ecological patterns. Yet, several aspects of soundscape diversity remain unexplored. Although some processes influencing acoustic niche usage may operate in the 24-hr temporal domain, most acoustic indices only capture the diversity of sounds co-occurring in sound files at a specific time of day. Moreover, many indices do not consider the relationship between the spectral and temporal traits of sounds simultaneously. To provide novel insights into landscape-scale patterns of acoustic niche usage at broader temporal scales, we present a workflow to quantify soundscape diversity through the lens of trait-based ecology. Our workflow quantifies the diversity of sound in the 24-hr acoustic trait space. We introduce the Operational Sound Unit (OSU), a unit of diversity measurement that groups sounds by their shared acoustic properties. Using OSUs and building on the framework of Hill numbers, we propose three metrics that capture different aspects of acoustic trait space usage: (i) soundscape richness, (ii) soundscape diversity and (iii) soundscape evenness. We demonstrate the use of these metrics by (a) simulating soundscapes to assess whether the indices possess a set of desirable behaviours and (b) quantifying soundscape richness and evenness along a gradient in species richness. We demonstrate that (a) the indices outlined herein have desirable behaviours and (b) the soundscape richness and evenness are positively correlated with the richness of sound-producing species. This suggests that more acoustic niche space is occupied when the species richness is higher. Additionally, species-poor acoustic communities have a higher proportion of rare sounds and use the acoustic space less evenly. Our workflow generates novel insights into acoustic niche usage at a landscape scale and provides a useful tool for biodiversity monitoring. Moreover, Hill numbers can also be used to measure the taxonomic, functional and phylogenetic diversity. Using a common framework for diversity measurement gives metrics a common behaviour, interpretation and standardised unit, thus ensuring comparisons between soundscape diversity and other metrics represent real-world ecological patterns rather than mathematical artefacts stemming from different formulae

Acoustic monitoring of Amazonian wildlife in human-modified landscapes

Tropical forest covers just 12% of the planet’s land surface, but disproportionately host the planet’s biodiversity, including around two thirds of all terrestrial species. Amazonia retains the largest extent of remaining tropical forest globally, but just over 50% of all tropical forest loss since 2002 has been in the region. Deforestation and disturbance result in significant loss in forest biodiversity, but quantifying the exact nature of those changes can be complex. The Amazon represents a particularly challenging case in which to assess biodiversity change due to the spatiotemporal scales being assessed, because of the high proportion of rare species, and the challenging conditions for conducting biodiversity surveys in tropical forest. Ecoacoustics has been championed as a valuable tool to overcome the difficulties of monitoring in such conditions and at large spatio-temporal scales, but applied analytical methods often remain underdeveloped. In this this thesis I develop and use a range of ecoacoustic methods to help understand the impact of anthropogenic disturbance on Amazonian wildlife, using an extensive audio dataset collected from survey points spanning a degradation gradient in the Eastern Brazilian Amazon. In Chapter 2 I introduce a quick and simple method for the detection of rainfall, tested for efficacy globally and with an accompanying R package. In Chapter 3 I present a new approach to subsampling of acoustic data for manual assessment of avian biodiversity, finding that using a high number of short repeat samples can detect approximately 50% higher alpha diversity than more commonly used approaches. In Chapter 4 I assess the sensitivity and fidelity of two commonly used acoustic indices to biodiversity responses to forest disturbances, finding that measuring indices at narrower, ecologically appropriate time-frequency bins avoids problems with signal masking. In Chapter 5 I use a two-stage, random forest based method to build a multi-taxa classifier for the nocturnal avifaunal community in the study region, and use the classifier-derived data to reveal that the nocturnal bird community is largely robust to less intense forms of forest disturbance. Overall, in this thesis I demonstrate that ecoacoustics can be a highly effective method for inventorying and monitoring biodiversity in one of the most diverse and challenging regions on the planet