Voice activity detection in eco-acoustic data enables privacy protection and is a proxy for human disturbance

1. Eco-acoustic monitoring is increasingly being used to map biodiversity across large scales, yet little thought is given to the privacy concerns and potential scientific value of inadvertently recorded human speech. Automated speech de tection is possible using voice activity detection (VAD) models, but it is not clear how well these perform in diverse natural soundscapes. In this study we pre sent the first evaluation of VAD models for anonymization of eco-acoustic data and demonstrate how speech detection frequency can be used as one potential measure of human disturbance. 2. We first generated multiple synthetic datasets using different data preprocess ing techniques to train and validate deep neural network models. We evaluated the performance of our custom models against existing state-of-the-art VAD models using playback experiments with speech samples from a man, woman and child. Finally, we collected long-term data from a Norwegian forest heavily used for hiking to evaluate the ability of the models to detect human speech and quantify a proxy for human disturbance in a real monitoring scenario. 3. In playback experiments, all models could detect human speech with high accu racy at distances where the speech was intelligible (up to 10 m). We showed that training models using location specific soundscapes in the data preprocessing step resulted in a slight improvement in model performance. Additionally, we found that the number of speech detections correlated with peak traffic hours (using bus timings) demonstrating how VAD can be used to derive a proxy for human disturbance with fine temporal resolution. 4. Anonymizing audio data effectively using VAD models will allow eco-acoustic monitoring to continue to deliver invaluable ecological insight at scale, while minimizing the risk of data misuse. Furthermore, using speech detections as a proxy for human disturbance opens new opportunities for eco-acoustic moni toring to shed light on nuanced human–wildlife interactionspublishedVersio

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studiespublishedVersio

Snowmobile noise alters bird vocalization patterns during winter and pre-breeding season

Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non-breeding winter season, a less-studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre-breeding behaviours such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. Synthesis and applications: Our findings emphasize the need to consider noise impacts in the non-breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analysing large-scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities.Snowmobile noise alters bird vocalization patterns during winter and pre-breeding seasonpublishedVersio

Transforming the use of citizen science data for biodiversity conservation at different scales

Obtaining large amount of data on species’ characteristics such as their distribution, abundance or movement patterns is not only important for scientists to better understand species’ ecology but it is also fundamentally important for policymakers and environmental managers because it provides a knowledge platform to ensure successful biodiversity conservation. Citizen science (i.e. the involvement of volunteers who collect and/or process data as part of a scientific inquiry) data has recently gained attention among researchers as it can help researchers tackle research questions that otherwise could not be addressed without the involvement of large numbers of professional data collectors. Nevertheless, because volunteer recorders are highly motivated to encounter interesting wildlife the spatial and temporal patterns of records are not random or systematic and hence very different from the kind of rigorous random sampling protocol that scientists are used to work with. The aim of this thesis is to understand the citizen science observation generation process and then assess the performance of citizen science observations to infer ecological properties at different ecological scales. This thesis consists of five articles. The first study takes the example of hunters as a special case of citizen scientists and assesses their importance for biodiversity monitoring. In articles 2 and 3 I compile distribution maps of large mammals in Europe using citizen science data in tandem with other source of data to study species’ ecology at a macro scales (distribution). Finally, articles 4 and 5 show that if potential biases are accounted for using appropriate statistical methods, citizen science observations can give a good approximation of species’ ecology at micro scales (habitat patches). From this thesis two main conclusions emerge. Firstly, to transform how citizen science is used it is critical to understand the data generation process underlying the creation of the geo-localised citizen science observations in order to fully grasp the extent of the potential biases in citizen science data. Then citizen science data can be used at multiple scales if biases are accounted for using proper methodology. It is important to realize that the methodologies used to account for biases in citizen science data have to be scaled to the research question and to the data available as results could lead to misleading conclusions about the species’ ecology. Overall, the future of citizen science remains very bright and this thesis contributes to further understanding and making better use of it

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

Hunters as citizen scientists: Contributions to biodiversity monitoring in Europe

Monitoring biodiversity characteristics at large scales and with adequate resolution requires considerable effort and resources. Overall, there is clearly a huge scope for European hunters, a special and often overlooked group of citizen scientist, to contribute even more to biodiversity monitoring, especially because of their presence across the entire European landscape. Using the Essential Biodiversity Variables (EBVs) framework we reviewed the published and grey literature and contacted experts to provide a comprehensive overview of hunters’ contributions to biodiversity monitoring.We examined the methods used to collect data in hunter-based monitoring, the geographic and taxonomic extent of such contributions and the scientific output stemming from hunter-based monitoring data. Our study suggests that hunter-based monitoring is widely distributed across Europe and across taxa as 32 out of the 36 European countries included in our analysis involve hunters in the monitoring of at least one species group with ungulates and small game species groups which have the widest hunter-based monitoring coverage. We found that it is possible to infer characteristics on Genetic composition, Species population, Species traits and Community composition with data that are being routinely collected by hunters in at least some countries. The main types of data provided are hunting bags data, biological samples including carcasses of shot animals and non-invasive samplings and Observations for counts and indices. Hunters collect data on biodiversity in its key dimensions. Collaborations between hunters and scientists are fruitful and should be considered a standard partnership for biodiversity conservation. To overcome the challenges in the use of hunters’ data, more rigorous protocols for sampling data should be implemented and improvements made in data integration methods.publishedVersio

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

What form of human-wildlife coexistence is mandated by legislation?: A comparative analysis of international and national instruments

There are currently many controversies over the process of wildlife conservation, mainly focused on determining which forms of human-wildlife relationship should be endorsed by society. These differences often lead to legal discussions between lawmakers and stakeholders as result of misinterpretation of law. In this study, we examine the dominant conservation ideologies underpinning institutionalized wildlife conservation by exploring the moral basis underlying a broad range of national and international legislation. We used a teleological interpretative approach to explore the implicit and explicit intentions of legislative instruments. We found that a shift from a human-nature dualism to an integration paradigm occurred in the legal frameworks during the last 20-30years. A desire to improve the status of threatened species or ecosystems was clearly expressed in all legislation. However, the widespread mention of consumptive values seems to indicate no principled opposition between the notions of conservation and of sustainable use. We identified three different groups of legislation: (1) a small group containing largely protectionist instruments, (2) a group based on the main European nature conservation texts and, (3) a cluster incorporating almost all the post-Convention on Biological Diversity (CBD) legislation from around the world. The CBD was found to have had a major impact on the shaping of the modern legal instruments, reconciling the eco- and anthropocentric values at the heart of modern legal thinking. Overall, the dominant legal ideology seems to aim for a compromise between the interests of society and wildlife, allowing its sustainable use and steering for shared space

Hunters as citizen scientists: Contributions to biodiversity monitoring in Europe

Monitoring biodiversity characteristics at large scales and with adequate resolution requires considerable effort and resources. Overall, there is clearly a huge scope for European hunters, a special and often overlooked group of citizen scientist, to contribute even more to biodiversity monitoring, especially because of their presence across the entire European landscape. Using the Essential Biodiversity Variables (EBVs) framework we reviewed the published and grey literature and contacted experts to provide a comprehensive overview of hunters’ contributions to biodiversity monitoring. We examined the methods used to collect data in hunter-based monitoring, the geographic and taxonomic extent of such contributions and the scientific output stemming from hunter-based monitoring data. Our study suggests that hunter-based monitoring is widely distributed across Europe and across taxa as 32 out of the 36 European countries included in our analysis involve hunters in the monitoring of at least one species group with ungulates and small game species groups which have the widest hunter-based monitoring coverage. We found that it is possible to infer characteristics on Genetic composition, Species population, Species traits and Community composition with data that are being routinely collected by hunters in at least some countries. The main types of data provided are hunting bags data, biological samples including carcasses of shot animals and non-invasive samplings and Observations for counts and indices. Hunters collect data on biodiversity in its key dimensions. Collaborations between hunters and scientists are fruitful and should be considered a standard partnership for biodiversity conservation. To overcome the challenges in the use of hunters’ data, more rigorous protocols for sampling data should be implemented and improvements made in data integration methods

Hunters as citizen scientists: Contributions to biodiversity monitoring in Europe

Monitoring biodiversity characteristics at large scales and with adequate resolution requires considerable effort and resources. Overall, there is clearly a huge scope for European hunters, a special and often overlooked group of citizen scientist, to contribute even more to biodiversity monitoring, especially because of their presence across the entire European landscape. Using the Essential Biodiversity Variables (EBVs) framework we reviewed the published and grey literature and contacted experts to provide a comprehensive overview of hunters’ contributions to biodiversity monitoring.We examined the methods used to collect data in hunter-based monitoring, the geographic and taxonomic extent of such contributions and the scientific output stemming from hunter-based monitoring data. Our study suggests that hunter-based monitoring is widely distributed across Europe and across taxa as 32 out of the 36 European countries included in our analysis involve hunters in the monitoring of at least one species group with ungulates and small game species groups which have the widest hunter-based monitoring coverage. We found that it is possible to infer characteristics on Genetic composition, Species population, Species traits and Community composition with data that are being routinely collected by hunters in at least some countries. The main types of data provided are hunting bags data, biological samples including carcasses of shot animals and non-invasive samplings and Observations for counts and indices. Hunters collect data on biodiversity in its key dimensions. Collaborations between hunters and scientists are fruitful and should be considered a standard partnership for biodiversity conservation. To overcome the challenges in the use of hunters’ data, more rigorous protocols for sampling data should be implemented and improvements made in data integration methods