Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

Passive acoustic monitoring provides a fresh perspective on fundamental ecological questions

Passive acoustic monitoring (PAM) has emerged as a transformative tool for applied ecology, conservation and biodiversity monitoring, but its potential contribution to fundamental ecology is less often discussed, and fundamental PAM studies tend to be descriptive, rather than mechanistic. Here, we chart the most promising directions for ecologists wishing to use the suite of currently available acoustic methods to address long-standing fundamental questions in ecology and explore new avenues of research. In both terrestrial and aquatic habitats, PAM provides an opportunity to ask questions across multiple spatial scales and at fine temporal resolution, and to capture phenomena or species that are difficult to observe. In combination with traditional approaches to data collection, PAM could release ecologists from myriad limitations that have, at times, precluded mechanistic understanding. We discuss several case studies to demonstrate the potential contribution of PAM to biodiversity estimation, population trend analysis, assessing climate change impacts on phenology and distribution, and understanding disturbance and recovery dynamics. We also highlight what is on the horizon for PAM, in terms of near-future technological and methodological developments that have the potential to provide advances in coming years. Overall, we illustrate how ecologists can harness the power of PAM to address fundamental ecological questions in an era of ecology no longer characterised by data limitation

Management relevant applications of acoustic monitoring for Norwegian nature – The Sound of Norway

Sethi, S. S., Fossøy, F., Cretois, B. & Rosten, C. M. 2021. Management relevant applications of acoustic monitoring for Norwegian nature – The Sound of Norway. NINA Report 2064. Norwegian Institute for Nature Research. High quality, large scale, and long-term field data is required as a foundation for any successful evidence-based nature management scheme. Whilst traditionally this data has been painstakingly collected by hand, breakthroughs in microelectronics and machine learning have opened the door for fully automated methods of ecosystem monitoring. Acoustic monitoring has shown particular promise as an affordable means to obtaining high quality ecological data on vast scales, and an array of sophisticated methods for data collection and analysis have been developed in the past decade. In this report, we first survey existing literature in ecological acoustic monitoring through the lens of four Norwegian nature management priority areas: ecological base maps, green infrastructure, ecological condition, and species action plans. In each case, we detail the type of data needed for effective management, how acoustic monitoring can contribute to the desired goals, and identify the areas in which further research is required for acoustic monitoring to contribute to these priority areas. We find straightforward opportunities for automated vocalisation detection approaches to contribute species occurrence, abundance, and behavioural data at high resolutions and large scales to ecological base maps, green infrastructure, and species action plans. Additionally, we note that soundscape level analyses can provide new, holistic measures of ecosystem health which may improve measures of ecological condition. We then cover the design, implementation, and results from the Sound of Norway project; a fully autonomous acoustic monitoring network deployed across the nation. Using the first large scale deployment of Bugg, a state-of-the-art ecological acoustic monitoring system, we surveyed 41 sites across forest, semi-natural grasslands, and urban settings between July and November 2021. 58 355 hours of audio data were uploaded directly from the field over a mobile internet link and analysed in real-time using a bird vocalisation detection model (BirdNET) and a soundscape fingerprinting approach. Once the analyses had been processed in the cloud, results were delivered through an intuitive and interactive web dashboard and the full dataset was exported in a machine-readable format for more in-depth analyses. From expert annotations, we derived precision and recall metrics for the BirdNET model. The model had over 60% precision for 44 species (of which 21 species had 100% precision) and failed to identify any true calls for 5 species. Quantifying accuracy in this way gave us insight into strengths and weaknesses of the model and allowed us to control for potential misclassifications in downstream analyses. We used the filtered BirdNET detections to map species communities and changes in species richness across the full monitoring network, and to demonstrate that important phenological patterns could be derived from continuous acoustic monitoring data. We also demonstrated that high level soundscape fingerprints could be used to discern spatial and temporal patterns across our monitoring network, without the need for vocalisation detection models. Spatially, we showed that soundscape features differed across different land-use types through our network, and temporally, we showed that changes in the community driven by the seasons were represented in a similar way. Finally, we provide clear recommendations for how acoustic monitoring can best contribute to Norwegian nature management today. We identify existing monitoring programs which can, (i) benefit from the fine temporal resolution of acoustic data (e.g., TOVe, SEAPOP), (ii) integrate soundscape analyses to measure overall ecosystem health (e.g., ANO), and (iii) make use of audio based continuous measures of human disturbance (e.g., the national insect monitoring project). We then conclude by suggesting the most impactful directions for further methodological development to fine tune existing acoustic monitoring solutions to best serve the needs of Norwegian nature management.Sethi, S. S., Fossøy, F., Cretois, B. & Rosten, C. M. 2021. Management relevant applications of acoustic monitoring for Norwegian nature – The Sound of Norway. NINA Rapport 2064. Norsk institutt for naturforskning. Høykvalitets, storskala og langsiktige feltdata er et viktig grunnlag for å sikre en vellykket empirisk basert naturforvaltning. Tradisjonelt sett har disse dataene blitt innsamlet for hånd, men gjennombrudd innen mikroelektronikk og maskinlæring har nå muliggjort helautomatiserte metoder for overvåkig av økosystemer. Akustisk overvåking er en lovende og kostnadseffektiv metode for å samle inn økologiske data på stor skala, og det har blitt utviklet en rekke sofistikerte metoder for datainnsamling og analyse det siste tiåret. I denne rapporten starter vi med å kartlegge eksisterende litteratur innenfor økologisk akustikk med fokus på fire norske naturforvaltningsområder: økologiske grunnkart, grønn infrastruktur, økologisk tilstand og handlingsplaner for trua arter. I hvert tilfelle diskuterer vi hvilken type data som trengs for en effektiv forvaltning, hvordan akustisk overvåking kan bidra til å nå de ønskede målene, og identifiserer områder der ytterligere forskning er nødvendig for at akustisk overvåking skal kunne bidra til disse prioriterte områdene. Vi viser hvordan enkle automatiserte deteksjonsmetoder for lyd kan bidra med høyoppløselige data på artsforekomst, bestandsstørrelse og atferd på en stor skala til økologiske grunnkart, grønn infrastruktur og handlingsplaner for trua arter. I tillegg kan analyser av lydbilder gi nye, helhetlige mål på økosystemhelse og forbedre mål på økologisk tilstand. Vi rapporterer deretter oppsett, implementering og resultater fra Lyden av Norge-prosjektet; et helautonomt akustisk overvåkingsnettverk fordelt over store deler av landet. Vi presenterer den første storskala testen av BUGG, et toppmoderne økologisk akustisk overvåkingssystem, der vi overvåket 41 lokaliteter på tvers av skog, seminaturlig mark og mer urbane habitater mellom juli og november 2021. Totalt ble 58 355 timer med lyddata lastet opp direkte fra felt over en mobil internettkobling og analysert i sanntid ved hjelp av en deteksjonsmodell for fuglelyd (BirdNET) og en lydbilde-fingeravtrykkstilnærming. Når analysene var behandlet i skyen, ble resultatene levert gjennom et intuitivt og interaktivt online dashbord, og hele datasettet ble til slutt eksportert i et maskinlesbart format for mer dyptgående analyser. Vi utledet presisjons- og gjenkallingsmålinger for modellen ved hjelp av ekspertvurderinger. Modellen hadde over 60 % presisjon for 44 arter (hvorav 21 arter hadde 100 % presisjon) og identifiserte 5 arter der modellen gav falske positiver, altså at disse artene ikke fantes på lokaliteten. Å kvantifisere nøyaktighet på denne måten ga oss innsikt i styrker og svakheter ved modellen og tillot oss å kontrollere for potensielle feilklassifiseringer i videre analyser. Vi brukte de filtrerte BirdNET-deteksjonene for å kartlegge artssamfunn og endringer i artsrikdom på tvers av hele overvåkingsnettverket, og for å demonstrere at viktige fenologiske mønstre kan utledes fra kontinuerlige akustiske overvåkingsdata. I tillegg demonstrerte vi at lydbilder kan brukes til å beskrive mønstre i tid og rom på tvers av overvåkingsnettverket vårt. Fra disse analysene har vi vist at automatisert akustisk overvåking kan gi kontinuerlige økologiske data både for enkeltarter og på et overordnet samfunnsnivå, inkludert informasjon om biodiversitet, samfunnsendringer og migrasjonstidspunkt. Til slutt gir vi anbefalinger for hvordan akustisk overvåking kan benyttes av norsk naturforvaltning i dag. Vi identifiserer eksisterende overvåkingsprogrammer som kan, (i) dra nytte av den høye tidsmessige oppløsningen til akustiske data (f.eks. TOVe, SEAPOP), (ii) integrere lydbildeanalyser for å måle generell økosystemhelse (f.eks. ANO), og (iii) integrere lydbaserte kontinuerlige mål på menneskelig påvirkning (f.eks. nasjonal overvåking av insekter). Vi avslutter med å peke på de mest effektive løsningene for videre metodeutvikling som kan finjustere eksisterende akustiske overvåkingsløsninger og ivareta behovene til norsk naturforvaltning

Full-Stack Bioacoustics: Field Kit to AI to Action (Workshop report)

Acoustic data (sound recordings) are a vital source of evidence for detecting, counting, and distinguishing wildlife. This domain of "bioacoustics" has grown in the past decade due to the massive advances in signal processing and machine learning, recording devices, and the capacity of data processing and storage. Numerous research papers describe the use of Raspberry Pi or similar devices for acoustic monitoring, and other research papers describe automatic classification of animal sounds by machine learning. But for most ecologists, zoologists, conservationists, the pieces of the puzzle do not come together: the domain is fragmented. In this Lorentz workshop we bridge this gap by bringing together leading exponents of open hardware and open-source software for bioacoustic monitoring and machine learning, as well as ecologists and other field researchers. We share skills while also building a vision for the future development of "bioacoustic AI". This report contains an overview of the workshop aims and structure, as well as reports from the six groups

Full-Stack Bioacoustics:Field Kit to AI to Action (Workshop report)

Acoustic data (sound recordings) are a vital source of evidence for detecting, counting, and distinguishing wildlife. This domain of "bioacoustics" has grown in the past decade due to the massive advances in signal processing and machine learning, recording devices, and the capacity of data processing and storage. Numerous research papers describe the use of Raspberry Pi or similar devices for acoustic monitoring, and other research papers describe automatic classification of animal sounds by machine learning. But for most ecologists, zoologists, conservationists, the pieces of the puzzle do not come together: the domain is fragmented. In this Lorentz workshop we bridge this gap by bringing together leading exponents of open hardware and open-source software for bioacoustic monitoring and machine learning, as well as ecologists and other field researchers. We share skills while also building a vision for the future development of "bioacoustic AI". This report contains an overview of the workshop aims and structure, as well as reports from the six groups

Management relevant applications of acoustic monitoring for Norwegian nature – The Sound of Norway

Sethi, S. S., Fossøy, F., Cretois, B. & Rosten, C. M. 2021. Management relevant applications of acoustic monitoring for Norwegian nature – The Sound of Norway. NINA Report 2064. Norwegian Institute for Nature Research. High quality, large scale, and long-term field data is required as a foundation for any successful evidence-based nature management scheme. Whilst traditionally this data has been painstakingly collected by hand, breakthroughs in microelectronics and machine learning have opened the door for fully automated methods of ecosystem monitoring. Acoustic monitoring has shown particular promise as an affordable means to obtaining high quality ecological data on vast scales, and an array of sophisticated methods for data collection and analysis have been developed in the past decade. In this report, we first survey existing literature in ecological acoustic monitoring through the lens of four Norwegian nature management priority areas: ecological base maps, green infrastructure, ecological condition, and species action plans. In each case, we detail the type of data needed for effective management, how acoustic monitoring can contribute to the desired goals, and identify the areas in which further research is required for acoustic monitoring to contribute to these priority areas. We find straightforward opportunities for automated vocalisation detection approaches to contribute species occurrence, abundance, and behavioural data at high resolutions and large scales to ecological base maps, green infrastructure, and species action plans. Additionally, we note that soundscape level analyses can provide new, holistic measures of ecosystem health which may improve measures of ecological condition. We then cover the design, implementation, and results from the Sound of Norway project; a fully autonomous acoustic monitoring network deployed across the nation. Using the first large scale deployment of Bugg, a state-of-the-art ecological acoustic monitoring system, we surveyed 41 sites across forest, semi-natural grasslands, and urban settings between July and November 2021. 58 355 hours of audio data were uploaded directly from the field over a mobile internet link and analysed in real-time using a bird vocalisation detection model (BirdNET) and a soundscape fingerprinting approach. Once the analyses had been processed in the cloud, results were delivered through an intuitive and interactive web dashboard and the full dataset was exported in a machine-readable format for more in-depth analyses. From expert annotations, we derived precision and recall metrics for the BirdNET model. The model had over 60% precision for 44 species (of which 21 species had 100% precision) and failed to identify any true calls for 5 species. Quantifying accuracy in this way gave us insight into strengths and weaknesses of the model and allowed us to control for potential misclassifications in downstream analyses. We used the filtered BirdNET detections to map species communities and changes in species richness across the full monitoring network, and to demonstrate that important phenological patterns could be derived from continuous acoustic monitoring data. We also demonstrated that high level soundscape fingerprints could be used to discern spatial and temporal patterns across our monitoring network, without the need for vocalisation detection models. Spatially, we showed that soundscape features differed across different land-use types through our network, and temporally, we showed that changes in the community driven by the seasons were represented in a similar way. Finally, we provide clear recommendations for how acoustic monitoring can best contribute to Norwegian nature management today. We identify existing monitoring programs which can, (i) benefit from the fine temporal resolution of acoustic data (e.g., TOVe, SEAPOP), (ii) integrate soundscape analyses to measure overall ecosystem health (e.g., ANO), and (iii) make use of audio based continuous measures of human disturbance (e.g., the national insect monitoring project). We then conclude by suggesting the most impactful directions for further methodological development to fine tune existing acoustic monitoring solutions to best serve the needs of Norwegian nature management

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics (“functional connectivity”), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag s ¼ 34 s (partial Spearman correlation q ¼ 0:58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: q ¼ 0:43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations

catch22: CAnonical Time-series CHaracteristics

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Timeseries CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties