20 research outputs found
Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes
Sounding out ecoacoustic metrics: avian species richness is predicted by acoustic indices in temperate but not tropical habitats
Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the de- velopment of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures are lacking. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithol- ogists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (Root-mean-square, Spectral centroid and Zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses demonstrated that each habitat has a very distinct soundscape and that AIs track observed differences in habitat-dependent community composition. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors are significant contributors to avian diversity prediction in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking subtler habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks
Densely Connected CNNs for Bird Audio Detection
Detecting bird sounds in audio recordings automatically, if accurate enough, is expected to be of great help to the research community working in bio- and ecoacoustics, interested in monitoring biodiversity based on audio field recordings. To estimate how accurate the state-of-the-art machine learning approaches are, the Bird Audio Detection challenge involving large audio datasets was recently organized. In this paper, experiments using several types of convolutional neural networks (i.e. standard CNNs, residual nets and densely connected nets) are reported in the framework of this challenge. DenseNets were the preferred solution since they were the best performing and most compact models, leading to a 88.22% area under the receiver operator curve score on the test set of the challenge. Performance gains were obtained thank to data augmentation through time and frequency shifting, model parameter averaging during training and ensemble methods using the geometric mean. On the contrary, the attempts to enlarge the training dataset with samples of the test set with automatic predictions used as pseudo-groundtruth labels consistently degraded performance
LONG-TERM IMPACTS OF AMAZON FOREST DEGRADATION ON CARBON STOCKS AND ANIMAL COMMUNITIES: COMBINING SOUND, STRUCTURE, AND SATELLITE DATA
The Amazon forest plays a vital role in the Earth system, yet forest degradation from logging and fire jeopardizes carbon storage and biodiversity conservation along the deforestation frontier. Polices to reduce forest carbon emissions (REDD+) will fall short of their intended goals unless carbon and biodiversity losses from forest degradation can be monitored over time. Emerging remote sensing tools, lidar and ecoacoustics, provide a means to monitor carbon and biodiversity across spatial, temporal, and taxonomic scales to address data gaps on species distributions and time-scales for recovery. This dissertation draws from a novel multi-sensor perspective to characterize the long-term ecological legacy of Amazon forest degradation across a 20,000 km2 landscape in Mato Grosso, Brazil. It combines high-density airborne lidar, 1100 hours of acoustic surveys, and annual time series of Landsat data to pursue three complementary studies. Chapter 2 establishes the bedrock of the investigation by using fine-scale measurements of structure sampled across a large diversity of degraded forests to model the initial loss and time-dependent recovery of carbon stocks and habitat structure following fire and logging. Chapter 3 models the interactions between sound and structure to predict acoustic community variation, and to account for attenuation in dense tropical forests. Lastly, Chapter 4 uses sound to go beyond structure to identify the specific degradation sequences and pseudo-taxa that give rise to variation in the ‘acoustic guild’ over time. Soundscapes reveal strong and sustained shifts in insect assemblages following fire, and a decoupling of biotic and biomass recovery following logging that defy theoretical predictions (Acoustic Niche Hypothesis). The synergies between lidar and acoustic data confirm the long-term legacy of forest degradation on both forest structure and animal communities in frontier Amazon forests. After multiple fires, forests become carbon-poor, habitats become simplified, and animal communication networks became quieter, less connected, and more homogenous. The combined results quantify large potential benefits to protecting already-burned Amazon forests from recurrent fires. This dissertation paves the way for greater integration of remote sensing and analysis tools to enhance capabilities for bringing biomass and biodiversity monitoring to scale. Building on this research with species-level and multi-temporal measurements will reduce uncertainty around the breakpoints that drive carbon and biodiversity loss following degradation
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Rapid coral reef assessment using 3D modelling and acoustics: acoustic indices correlate to fish abundance, diversity and environmental indicators in West Papua, Indonesia
Background
Providing coral reef systems with the greatest chance of survival requires effective assessment and monitoring to guide management at a range of scales from community to government. The development of rapid monitoring approaches amenable to collection at community level, yet recognised by policymakers, remains a challenge. Technologies can increase the scope of data collection. Two promising visual and audio approaches are (i) 3D habitat models, generated through photogrammetry from video footage, providing assessment of coral cover structural metrics and (ii) audio, from which acoustic indices shown to correlate to vertebrate and invertebrate diversity, can be extracted.
Methods
We collected audio and video imagery using low cost underwater cameras (GoPro Hero7â„¢) from 34 reef samples from West Papua (Indonesia). Using photogrammetry one camera was used to generate 3D models of 4 m2 reef, the other was used to estimate fish abundance and collect audio to generate acoustic indices. We investigated relationships between acoustic metrics, fish abundance/diversity/functional groups, live coral cover and reef structural metrics.
Results
Generalized linear modelling identified significant but weak correlations between live coral cover and structural metrics extracted from 3D models and stronger relationships between live coral and fish abundance. Acoustic indices correlated to fish abundance, species richness and reef functional metrics associated with overfishing and algal control. Acoustic Evenness (1,200–11,000 Hz) and Root Mean Square RMS (100–1,200 Hz) were the best individual predictors overall suggesting traditional bioacoustic indices, providing information on sound energy and the variability in sound levels in specific frequency bands, can contribute to reef assessment.
Conclusion
Acoustics and 3D modelling contribute to low-cost, rapid reef assessment tools, amenable to community-level data collection, and generate information for coral reef management. Future work should explore whether 3D models of standardised transects and acoustic indices generated from low cost underwater cameras can replicate or support ‘gold standard’ reef assessment methodologies recognised by policy makers in marine management
Automated bioacoustics:methods in ecology and conservation and their potential for animal welfare monitoring
Vocalizations carry emotional, physiological and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation and sound scene classification. These methods are already used to automatically classify animal vocalizations, for example, in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation and sound classification. Following this, we detail the vocalizations produced by three of the most important farm livestock species: chickens (Gallus gallus domesticus), pigs (Sus scrofa domesticus) and cattle (Bos taurus). Finally, we describe how these methods can be applied to monitor animal welfare with new potential for developing automated methods for large-scale farming
The avian dawn chorus across Great Britain: using new technology to study breeding bird song
The avian dawn chorus is a period of high song output performed daily around sunrise during the breeding season. Singing at dawn is of such significance to birds that they remain motivated to do so amid the noise of numerous others. Yet, we still do not fully understand why the dawn chorus exists. Technological advances in recording equipment, data storage and sound analysis tools now enable collection and scrutiny of large acoustic datasets, encouraging research on sound-producing organisms and promoting ‘the soundscape’ as an indicator of ecosystem health. Using an unrivalled dataset of dawn chorus recordings collected during this thesis, I explore the chorus throughout Great Britain with the prospect of furthering our understanding and appreciation of this daily event. I first evaluate the performance of four automated signal recognition tools (‘recognisers’) when identifying the singing events of target species during the dawn chorus, and devise a new ensemble approach that improves detection of singing events significantly over each of the recognisers in isolation. I then examine daily variation in the timing and peak of the chorus across the country in response to minimum overnight temperature. I conclude that cooler temperatures result in later chorus onset and peak the following dawn, but that the magnitude of this effect is greater at higher latitude sites with cooler and less variable overnight temperature regimes. Next, I present evidence of competition for acoustic space during the dawn chorus between migratory and resident species possessing similar song traits, and infer that this may lead either to fine-scale temporal partitioning of song, such that each competitor maintains optimal output, or to one competitor yielding. Finally, I investigate day-to-day attenuation of song during the leaf-out period from budburst through to full-leaf in woodland trees, and establish the potential for climate-driven advances in leaf-out phenology to attenuate song if seasonal singing activity in birds has not advanced to the same degree. I find that gradual attenuation of sound through the leaf-out process is dependent on the height of the receiver, and surmise that current advances in leaf-out phenology are unlikely to have undue effect on song propagation. This project illustrates the advantage of applying new technology to ecological studies of complex acoustic environments, and highlights areas in need of improvement, which is essential if we are to comprehend and preserve our natural soundscapes
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards:
• regional, continental and global sharing of ecological data,
• thorough integration of complementing monitoring technologies including DNA-barcoding,
• sophisticated pattern recognition by deep learning,
• advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling,
• decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
Passive acoustic monitoring for assessment of natural and anthropogenic sound sources in the marine environment using automatic recognition
In the marine environment, sound can be an efficient source of information. Indeed, several marine species, including fish, use sound to navigate, select habitats, detect predators and prey, and to attract mates. Therefore, all the abiotic, biotic and manmade sounds that comprise the soundscape, have the potential to be used to assess and monitor species and marine environments. Passive acoustic monitoring (PAM) involves the use of acoustic sensors to record sound in the environment, from which relevant ecological information can be inferred. This thesis studied marine soundscapes, with special attention on fish communities, anthropogenic noise, and applied several methods to analyse acoustic recordings. Most of the focus was on the Tagus estuary, where the presence of two highly vocal species is known: the Lusitanian toadfish (Halobatrachus didactylus) and the meagre (Argyrosomus regius). Azorean and Mozambique soundscapes were also analysed. Several methods were applied to extract information and to visualize soundscape characteristics, including sound recognition systems based on hidden Markov models to recognize fish sounds and boat passages. Analysis of several types of marine environments and time scales showed several advantages and disadvantages of different methods. The use of sound pressure level on different frequency bands allowed the quantification of daily and seasonal patterns. Ecoacoustic indices appear to be cost-effective tools to monitor biodiversity in some marine environments. Using automatic recognition, vocal rhythms (diel and seasonal patterns) and vocal interactions among individuals were also characterized. Furthermore, boat noise effects on fish were studied: we encountered impacts on the audition, vocal behaviour and reproduction. Overall, we used PAM as a tool to remotely assess and monitor soundscapes, biodiversity, fish communities’ seasonal patterns, fish behaviour, species presence, and the effect of anthropogenic noise aiming to contribute for the management and conservation of marine ecosystems