02807nas a2200217 4500008004100000245008000041210007100121520213700192653002102329653001702350653001602367653001802383653002002401653002302421100002902444700001802473700002002491700001402511700001802525856004602543 2022 eng d00aSpatial dynamics of soundscapes and biodiversity in a semi‐arid landscape0 aSpatial dynamics of soundscapes and biodiversity in a semi‐arid 3 a

Context
Semi-arid landscapes are naturally hetero- geneous with several factors influencing this varia- tion. Fauna responses and adaptations vary in xeric environments, and the scale of observation is impor- tant. Biodiversity monitoring at several scales can be challenging, and acoustics are an alternative to this issue.

Objectives
We investigated how audible biodiver- sity is influenced by environmental factors (e.g.: veg- etation metrics, climatic variables, etc.) across a fine spatial scale, aiming to provide a better understand- ing of the variation in audible species across record- ing locations placed close together. These results will improve the current knowledge on ecoacoustics as a tool for measuring ecological processes in this biome, and better inform conservation plans.

Methods
We collected data in the semi-arid region in Queensland, Australia placing 24 recorders 200 m apart for 48 h. We also sampled environmental attributes (e.g.: temperature and vegetation structure metrics) and used acoustic indices in a time-series algorithm to categorise sound into classes. Bird spe- cies and feeding guilds were also identified.

Results We found significant differences between proximate sensors, demonstrating that soundscape differences occur across fine spatial scales. Birds and insects were the predominant biophonic sound observed and both groups were associated with shrub cover and subcanopy height. Environments with higher shrub and subcanopy cover had a higher per- centage of all birds’ feeding guilds and insects. Sixty- three bird species were identified, including a threat- ened bird species in Queensland.

Conclusion
We show biodiversity is influenced by vegetation heterogeneity across fine spatial scales in semi-arid regions, identifying which attributes sus- tain higher levels of biodiversity activity. Our study reveals the practicality of acoustic surveys for this biodiversity monitoring by covering a large area in 48 h. However, we caution that scale is an important consideration when designing surveys.

10aacoustic indices10abiodiversity10aShrub cover10aSpatial scale10aSubcanopy cover10aVegetation mosaics1 aScarpelli, Marina, D. A.1 aTucker, David1 aDoohan, Brendan1 aRoe, Paul1 aFuller, Susan uhttps://bio.acousti.ca/zh-hant/node/5846802769nas a2200205 4500008004100000245011800041210006900159520205100228653003002279653001702309653002602326653002102352100002302373700002402396700001702420700001502437700002202452700002202474856006702496 2020 eng d00aAcoustic Complexity Index to assess benthic biodiversity of a partially protected area in the southwest of the UK0 aAcoustic Complexity Index to assess benthic biodiversity of a pa3 a

The soundscape of the marine environment is a relatively understudied area of ecology that has the potential to provide large amounts of information on biodiversity, reproductive behaviour, habitat selection, spawning and predator–prey interactions. Biodiversity is often visually assessed and used as a proxy for ecosystem health. Visual assessment using divers or remote video methods can be expensive, and limited to times of good weather and water visibility. Previous studies have concluded that acoustic measures, such as the Acoustic Complexity Index (ACI), correlate with visual biodiversity estimates and offer an alternative to assess ecosystem health.

Here, the ACI measured over 5 years in a Marine Protected Area (MPA) in the UK, Lyme Bay, was analysed alongside another monitoring method, Baited Remote Underwater Video Systems (BRUVs). Two treatments were sampled annually in the summer from 2014 until 2018 with sites inside the MPA, as well as Open Control sites outside of the MPA.

Year by year correlations, which have been used elsewhere to test ACI, showed significant correlations with Number of Species and ACI. However, the sign of these correlations changed almost yearly, showing that more in-depth analyses are needed.

Multivariate analysis of the benthic assemblage composition (from BRUVs) was carried out by Permutational Multivariate Analysis of Variance (PERMANOVA) using Distance Matrices. Although not consistently correlating with univariate measures, the ACI was significantly interacting with the changing benthic assemblage composition, as it changed over time and protection (Inside vs Outside the MPA).

ACI showed potential to allude to shifting benthic communities, yet with no consistency when used alongside univariate measures of diversity. Although it is not without its own disadvantages, and thus should be developed further before implementation, the ACI could potentially reflect more complex changes to the benthos than simply the overall diversity.

10aAcoustic Complexity Index10abiodiversity10aMarine Protected Area10aMonitoring Tools1 aDavies, Bede, F.R.1 aAttrill, Martin, J.1 aHolmes, Luke1 aRees, Adam1 aWitt, Matthew, J.1 aSheehan, Emma, V. uhttps://linkinghub.elsevier.com/retrieve/pii/S1470160X1931015502781nas a2200181 4500008004100000245010000041210006900141520217000210653002102380653001702401653002302418653002102441653002202462653002202484100001902506700001302525856006102538 2019 eng d00aSource separation in ecoacoustics: A roadmap towards versatile soundscape information retrieval0 aSource separation in ecoacoustics A roadmap towards versatile so3 a

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source‐specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model‐based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model‐based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model‐based techniques can yield state‐of‐the‐art separation performance, accordingly facilitate content‐based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high‐performance source separation for ecoacoustics, and accordingly improve soundscape‐based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross‐disciplinary collaboration.

10aacoustic habitat10abiodiversity10aecosystem dynamics10amachine learning10apassive acoustics10aSignal processing1 aLin, Tzu‐Hao1 aTsao, Yu uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.14102252nas a2200181 4500008004100000245010500041210006900146520164300215653002101858653001701879653001701896653001501913653001801928653001401946100002101960700001801981856007101999 2019 eng d00aA soundscape assessment of the Sasso Fratino Integral Nature Reserve in the Central Apennines, Italy0 asoundscape assessment of the Sasso Fratino Integral Nature Reser3 a

Ecoacoustics investigates natural and anthropogenic sounds and their relationship with the environment. It is a powerful tool for biodiversity monitoring, management and conservation and also with regards to the global climate change issue. This study, based on data collected in 2017, describes for the first time the soundscape of the Sasso Fratino Integral Nature Reserve (INR) in Italy, an area characterised by the almost absence of anthropogenic noise, where we selected three recording sites within and adjacent the reserve. We adopted a double approach: one qualitative, based on visual screening of compact daily spectrograms; the other quantitative, by generating acoustic indices. In general, all sites are characterised by quiet nights and very acoustically dense daylight hours, with a composite biophony occupying the range 1500–9000 Hz. Moreover, the principal component analysis shows that the sites inside and outside the reserve are well differentiated and distinctly clustered, which could be due to their spatial heterogeneity and to the biophony’s different contributions. In this case, our results agree with the recognition of sonic patterns, or sonotopes, related to the different overlapping of biotic and abiotic sonic agents. The long-term acoustic data collection allows a reference repository to be built for monitoring the INR’s biophony status and evolution: as any human presence or intervention is currently prohibited, only external global changes could be considered as possible factors influencing any shift in the species’ presence and distribution inside the reserve.

10aacoustic indices10abiodiversity10aecoacoustics10amonitoring10aNational Park10asonotopes1 aRighini, Roberta1 aPavan, Gianni uhttps://www.tandfonline.com/doi/full/10.1080/14888386.2019.1696229