/pdf/guidelines-for-the-use-of-acoustic-indices-in-environmental-2zauk3ad39.pdf

Guidelines for the use of acoustic indices in environmental research

Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.

/pdf/autonomous-sound-recording-outperforms-human-observation-for-2m7bd8rg23.pdf

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide.

Knowledge that can be gained from acoustic data collection in tropical ecosystems is low-hanging fruit. There is every reason to record and with every day, there are fewer excuses not to do it. In recent years, the cost of acoustic recorders has decreased substantially (some can be purchased for under US$50, e.g., Hill et al. 2018) and the technology needed to store and analyze acoustic data is continuously improving (e.g., Corrada Bravo et al. 2017, Xie et al. 2017). Soundscape recordings provide a permanent record of a site at a given time and contain a wealth of invaluable and irreplaceable information. Although challenges remain, failure to collect acoustic data now in tropical ecosystems would represent a failure to future generations of tropical researchers and the citizens that benefit from ecological research. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe the types of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both shortand long-term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings.

/pdf/it-s-time-to-listen-there-is-much-to-be-learned-from-the-52209u8mko.pdf

It's Time to Listen: There is Much to be Learned from the Sounds of Tropical Ecosystems

Recording of forest soundscapes can help monitor animal biodiversity for conservation Conservation areas around the world aim to help conserve animal biodiversity, but it is often difficult to measure conservation success without detailed on-the-ground surveys. High-resolution satellite imagery can be used to verify whether or not deforestation has occurred in areas dedicated for conservation (1). Such remote sensing analyses can reveal forest loss and, in some cases, severe forest degradation, such as through fragmentation and intensive selective logging, especially if it includes the construction of roads or camps. However, conservation benefit is determined not only by forest loss but also by the level of degradation in those forests left standing. Bioacoustics—specifically the recording and analysis of entire soundscapes—is an emerging tool with great promise for effectively monitoring animal biodiversity in tropical forests under various conservation schemes (2, 3).

https://science.sciencemag.org/content/sci/363/6422/28.full.pdf

The sound of a tropical forest

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.

/pdf/automated-bioacoustics-methods-in-ecology-and-conservation-1s2d77gc9o.pdf

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic-index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random-forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R2  ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random-forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R2 ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.

https://conbio.onlinelibrary.wiley.com/doi/pdf/10.1111/cobi.13119

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring is a cornerstone of the assessment of ecosystem health and the improvement of automated assessment systems has the potential to have a transformative impact on global biodiversity monitoring, at a scale and level of detail that is impossible with manual annotation or other more traditional methods. The BirdCLEF challenge-as part of the 2020 LifeCLEF Lab [12]-focuses on the development of reliable detection systems for avian vocalizations in continuous soundscape data. The goal of the task is to localize and identify all audible birds within the provided soundscape test set. This paper describes the methodology of the conducted evaluation as well as the synthesis of the main results and lessons learned.

/pdf/overview-of-birdclef-2020-bird-sound-recognition-in-complex-3gkqsz24m1.pdf

Mary Clapp

Papers

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Overview of BirdCLEF 2020: Bird Sound Recognition in Complex Acoustic Environments.