Using a novel visualization tool for rapid survey of long-duration acoustic recordings for ecological studies of frog chorusing

Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species

Sequential information in a great ape utterance

Birdsong is a prime example of acoustically sophisticated vocal behaviour, but its complexity has evolved mainly through sexual selection to attract mates and repel sexual rivals. In contrast, non-human primate calls often mediate complex social interactions, but are generally regarded as acoustically simple. Here, we examine arguably the most complex call in great ape vocal communication, the chimpanzee (Pan troglodytes schweinfurthii) â € pant hoot'. This signal consists of four acoustically distinct phases: introduction, build-up, climax and let-down. We applied state-of-The-Art Support Vector Machines (SVM) methodology to pant hoots produced by wild male chimpanzees of Budongo Forest, Uganda. We found that caller identity was apparent in all four phases, but most strongly in the low-Amplitude introduction and high-Amplitude climax phases. Age was mainly correlated with the low-Amplitude introduction and build-up phases, dominance rank (i.e. social status) with the high-Amplitude climax phase, and context (reflecting activity of the caller) with the low-Amplitude let-down phase. We conclude that the complex acoustic structure of chimpanzee pant hoots is linked to a range of socially relevant information in the different phases of the call, reflecting the complex nature of chimpanzee social lives

N-Eared listening: transdisciplinary ecoacoustics at human-environment interfaces

Understanding, managing and improving human-environment interactions is amongst the most pressing chal- lenges of our time. However, this is a wicked problem that we need to approach from multiple perspectives. I will outline a series of case-studies in inter- and transdisciplinary soundscape research: from validating ecoacoustic metrics in temperate UK woodlands, through wilderness mapping in the Swedish Arctic and community-led reef restoration in Indonesia to indigenous-led cultural heritage projects in the Ecuadorian Amazon. In doing so I aim to illustrate the value of integrating different forms of knowledge across disciplines, everyday and indigenous practices and speculate that triangulating ecological theory and computational methods with human experience might point to valuable new approaches to ecoaoustic analysis and even inspire fresh consideration of core con- servation imperatives, in order to better align the anthroposphere and biosphere for the benefit of all

Evaluation of Deep Learning-Based Monitoring of Frog Reproductive Phenology

カエルの鳴き声をAIで識別する --繁殖活動の高効率なモニタリング調査に向けて--. 京都大学プレスリリース. 2023-11-20.To evaluate the utility of a deep-learning approach for monitoring amphibian reproduction, we examined the classification accuracy of a trained model and tested correlations between calling intensity and frog abundance. Field recording and count surveys were conducted at two sites in Kyoto City, Japan. A convolutional neural network (CNN) model was trained to classify the calls of five anuran species. The model achieved 91–100% precision and 75–98% recall per species, with relatively lower performance on less abundant species. Computational experiments investigating the effects of the number and seasonality of the training samples showed that models trained on larger datasets from broader recording seasons performed better. Calling activity was high when males were abundant (Pearson's r = 0.45–0.66), although correlations between the calling activity and the number of pairs in amplexus were generally weaker. Our results suggest that deep learning is an effective tool for reconstructing the reproductive phenology of male anurans from field recordings. However, caution is required when applying to rare species and when inferring female reproductive activity

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

Listening forward: approaching marine biodiversity assessments using acoustic methods

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mooney, T. A., Di Iorio, L., Lammers, M., Lin, T., Nedelec, S. L., Parsons, M., Radford, C., Urban, E., & Stanley, J. Listening forward: approaching marine biodiversity assessments using acoustic methods. Royal Society Open Science, 7(8), (2020): 201287, doi:10.1098/rsos.201287.Ecosystems and the communities they support are changing at alarmingly rapid rates. Tracking species diversity is vital to managing these stressed habitats. Yet, quantifying and monitoring biodiversity is often challenging, especially in ocean habitats. Given that many animals make sounds, these cues travel efficiently under water, and emerging technologies are increasingly cost-effective, passive acoustics (a long-standing ocean observation method) is now a potential means of quantifying and monitoring marine biodiversity. Properly applying acoustics for biodiversity assessments is vital. Our goal here is to provide a timely consideration of emerging methods using passive acoustics to measure marine biodiversity. We provide a summary of the brief history of using passive acoustics to assess marine biodiversity and community structure, a critical assessment of the challenges faced, and outline recommended practices and considerations for acoustic biodiversity measurements. We focused on temperate and tropical seas, where much of the acoustic biodiversity work has been conducted. Overall, we suggest a cautious approach to applying current acoustic indices to assess marine biodiversity. Key needs are preliminary data and sampling sufficiently to capture the patterns and variability of a habitat. Yet with new analytical tools including source separation and supervised machine learning, there is substantial promise in marine acoustic diversity assessment methods.Funding for development of this article was provided by the collaboration of the Urban Coast Institute (Monmouth University, NJ, USA), the Program for the Human Environment (The Rockefeller University, New York, USA) and the Scientific Committee on Oceanic Research. Partial support was provided to T.A.M. from the National Science Foundation grant OCE-1536782

INDICES AND ECOINFORMATICS TOOLS FOR THE STUDY OF SOUNDSCAPE DYNAMICS

The study of the new field of soundscape ecology presents several research avenues to explore. In the chapters in this dissertation, I have followed several of the technical and methodological areas of the study of soundscapes. In Chapter 1, I presented the current status of the study of soundscapes as well as an overview of the contributions made in this dissertation to the field. These contributions were framed in the definition of ecological informatics (Michener and Jones 2012). In Chapter 2, I developed a web-based system to manage audio archives. This system organizes thousands of audio files while collecting the necessary metadata

Identificación de múltiples especies en paisajes acústicos usando técnicas de aprendizaje no supervisado

RESUMEN : En este trabajo de grado, se propone un método no supervisado y multiclase para identificar llamadas de especies de diferentes animales en un paisaje sonoro. Esta metodología no requiere conocimiento previo de las fuentes bióticas para ser identificadas y es capaz de encontrar el grado de relación entre ellas. Para ello se usa un algoritmo de agrupamiento difuso (LAMDA- Learning Algorithm for multivariate analysis) con un operador Yager-Ribalov 3π, cuya naturaleza difusa permite obtener el grado de pertenencia entre clases y así categorizar las fuentes de sonido en base a sus similitudes. Este método se presenta como una forma novedosa de monitoreo acústico el cual permite hacer seguimiento de manera pasiva de los cambios presentados en un ecosistema y su biodiversidad

Using acoustic indices in ecology : guidance on study design, analyses and interpretation

TBL was supported by Leverhulme Trust, research grant number RPG-2020-160; the Lorentz Centre, Leiden, The Netherlands; and UKAN+. AE and OM were supported by UKAN+.The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data. Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value. To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices. Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research.Publisher PDFPeer reviewe