The call of the wild: Investigating the potential for ecoacoustic methods in mapping wilderness areas

Carruthers-Jones, Jonathan; Eldridge, Alice; Guyot, Patrice; Hassall, Christopher; Holmes, George

doi:10.1016/j.scitotenv.2019.133797

Cited by 20 publications

(7 citation statements)

References 52 publications

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“…The first issue relates to their sensitivity to changes in environmental conditions. Acoustic indices have been shown to effectively distinguish between disparate land uses (Bradfer‐Lawrence et al, 2019; Carruthers‐Jones et al., 2019; Depraetere et al., 2012). However, they are less successful in distinguishing differences between similar land uses; for example between different types of forest (Bormpoudakis et al., 2013; Do Nascimento et al., 2020; Eldridge et al., 2018), or require a very large number of spatial replications to do so (Mitchell et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

et al. 2020

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Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land‐use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less‐sensitive acoustic groups, or target taxa sonification is masked by non‐target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged‐and‐burned and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0–22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time–frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.

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Section: Introductionmentioning

confidence: 99%

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

et al. 2020

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“…In accordance with other studies indicating that more intensively managed or disturbed habitats had lower acoustic diversity (Burivalova et al 2018;Gasc et al 2018;Myers et al 2019), we found less diverse soundscapes in landscapes dominated by homogeneous farmland with low edge density than in more complex mosaics of high edge density mixing crops, hedgerows and grasslands in Occitanie. Surprisingly, we found that periurban gardens had high bioacoustic activity measured by BI in Arizona while NDSI or H were low in the same sites, which may be an indication of high acoustic coexistence between anthrophony and biophony (Sueur et al 2008;Carruthers-Jones et al 2019). In southern Arizona, the extensive use of bird feeders and the low urban density allow gardens to support species-rich urban bird communities, including several hummingbird species (Trochilidae) whose buzzes produce acoustic signals that are typical components of garden soundscapes in semi-arid regions (Emlen 1974;McCaffrey and Wethington 2008; see Appendix S6).…”

Section: Effect Of Habitat Type On Acoustic Diversitymentioning

confidence: 62%

“…However, ACI does not account for constant sound signal produced by many arthropods (Pieretti et al 2011;Gasc et al 2015;Fairbrass et al 2017) and can therefore display inverse responses to habitat heterogeneity and bird diversity, especially in grasslands (Shamon et al 2021). While most AIs can be potentially biased by high road traffic noise in urban habitats, ACI was specifically developed to be impervious to constant sound (Pieretti et al 2011;Fairbrass et al 2017;Carruthers-Jones et al 2019;Bradfer-Lawrence et al 2020;Ross et al 2021). Using NDSI and ADI, Machado et al (2017) concluded that they were both affected by the distance with anthropic sound sources.…”

Section: Relative Performance Of Acoustic Indices In Complex Soundscapesmentioning

confidence: 99%

Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes

et al. 2022

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Context There is a long-standing quest in landscape ecology for holistic biodiversity metrics accounting for multi-taxa diversity in heterogeneous habitat mosaics. Passive Acoustic Monitoring of biodiversity may provide integrative indices allowing to investigate how soundscapes are shaped by compositional and configurational heterogeneity of mosaic landscapes.Objectives We tested the effects of dominant habitat and landscape heterogeneity on acoustic diversity indices across a large range of mosaic landscapes from two long-term socio-ecological research areas in Occitanie, France and Arizona, USA.Methods We assessed acoustic diversity by automated recording for 44 landscapes distributed along gradients of compositional and configurational heterogeneity. We analyzed the responses of six acoustic indices and a composite multiacoustic index to habitat type and multi-scale landscape metrics for three time periods: 24hr-diel cycles, dawns and nights. Results Landscape mosaics dominated by permanent grasslands in Occitanie and woodlands inArizona produced the highest values of acoustic diversity. Moreover, several indices including H, ADI, NDSI, NP and the multiacoustic index consistently responded to edge density in both study regions, but with contrasting patterns, increasing in Occitanie and decreasing in Arizona. Landscape configuration was a key driver of acoustic diversity for diel and nocturnal soundscapes, while dawn soundscapes depended more on landscape composition.Conclusions Acoustic diversity correlated more with configurational than compositional heterogeneity in both regions, with contrasting effects explained by the interplay between biogeography and land use history. We suggest that multiple acoustic indices are needed to properly account for complex responses of soundscapes to large-scale habitat heterogeneity in mosaic landscapes.

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“…Acoustic data analysis approaches are varied and include linear mixed models (Francomano et al, 2020), mean differences (Carruthers-Jones et al, 2019), manual inspection and tagging species or groups of interest (Ferreira et al, 2018) or using non-index based metrics (like amplitude and frequency) direct from sound files (Furumo and Aide, 2019). Despite the variety of ways to analyze sound data, single index approach is still one of the most common approaches.…”

Section: Discussionmentioning

confidence: 99%

Multi-Index Ecoacoustics Analysis for Terrestrial Soundscapes: A New Semi-Automated Approach Using Time-Series Motif Discovery and Random Forest Classification

et al. 2021

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High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis and interpretation of the big data produced remains a challenge. Computer-generated acoustic indices potentially provide a biologically meaningful summary of sound, however, temporal autocorrelation, difficulties in statistical analysis of multi-index data and lack of consistency or transferability in different terrestrial environments have hindered the application of those indices in different contexts. To address these issues we investigate the use of time-series motif discovery and random forest classification of multi-indices through two case studies. We use a semi-automated workflow combining time-series motif discovery and random forest classification of multi-index (acoustic complexity, temporal entropy, and events per second) data to categorize sounds in unfiltered recordings according to the main source of sound present (birds, insects, geophony). Our approach showed more than 70% accuracy in label assignment in both datasets. The categories assigned were broad, but we believe this is a great improvement on traditional single index analysis of environmental recordings as we can now give ecological meaning to recordings in a semi-automated way that does not require expert knowledge and manual validation is only necessary for a small subset of the data. Furthermore, temporal autocorrelation, which is largely ignored by researchers, has been effectively eliminated through the time-series motif discovery technique applied here for the first time to ecoacoustic data. We expect that our approach will greatly assist researchers in the future as it will allow large datasets to be rapidly processed and labeled, enabling the screening of recordings for undesired sounds, such as wind, or target biophony (insects and birds) for biodiversity monitoring or bioacoustics research.

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The call of the wild: Investigating the potential for ecoacoustic methods in mapping wilderness areas

Abstract: This is a repository copy of The Call of the Wild: investigating the potential for ecoacoustic methods in mapping wilderness areas.

Cited by 20 publications

References 52 publications

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes

Multi-Index Ecoacoustics Analysis for Terrestrial Soundscapes: A New Semi-Automated Approach Using Time-Series Motif Discovery and Random Forest Classification

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