A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data

Abstract

The field of bioacoustics is rapidly developing and characterized by diverse methodologies, approaches and aims. For instance, bioacoustics encompasses studies on the perception of pure tones in meticulously controlled laboratory settings, documentation of species’ presence and activities using recordings from the field, and analyses of circadian calling patterns in animal choruses. Newcomers to the field are confronted with a vast and fragmented literature, and a lack of accessible reference papers or textbooks. In this paper we contribute towards filling this gap. Instead of a classical list of “dos” and “don’ts”, we review some key papers which, we believe, embody best practices in several bioacoustic subfields. In the first three case studies, we discuss how bioacoustics can help identify the ‘who’, ‘where’ and ‘how many’ of animals within a given ecosystem. Specifically, we review cases in which bioacoustic methods have been applied with success to draw inferences regarding species identification, population structure, and biodiversity. In fourth and fifth case studies, we highlight how structural properties in signal evolution can emerge via ecological constraints or cultural transmission. Finally, in a sixth example, we discuss acoustic methods that have been used to infer predator–prey dynamics in cases where direct observation was not feasible. Across all these examples, we emphasize the importance of appropriate recording parameters and experimental design. We conclude by highlighting common best practices across studies as well as caveats about our own overview. We hope our efforts spur a more general effort in standardizing best practices across the subareas we’ve highlighted in order to increase compatibility among bioacoustic studies and inspire cross-pollination across the discipline.

Same data, different results? Evaluating machine learning approaches for individual identification in animal vocalisations

“…There is currently no published information about the potential variation in results that may derive from using different extraction and classification method combinations for determining individual identity in bioacoustics. Previous research has drawn attention to this issue and call for a unification of the methods used 16,17 and several excellent studies have investigated the impact of different statistics 7,14 or feature extraction methods 18 on the degree of individual identity quantified in vocalizations 18 . However, none of the above have considered the combined effects of feature extraction and classification methods on results.…”

“…The ease of application of various classifiers, including those using machine learning techniques, has provided many opportunities within the field of bioacoustics [11][12][13] . As a consequence, a plethora of methods is currently used with no consensus or consistent protocol 14,15 (although, some general guidelines and good practices have been put forward 7,15,16 ). One reason could be that individuality can be encoded in different ways, which varies by species and context 12 , making it difficult to generalise methods 12 .…”

Evaluating the potential of bioacoustics in avian migration research by citizen science and weather radar observations

“…However, such observations are mostly obtained in daylight and not simultaneously at night. With technological advances in recent decades and with improved accessibility and affordability of acoustic tools, sound recordings have steeply increased in popularity [7]. In recent years, the study of nocturnal calls has strongly developed on the American continent (e.g.…”

Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners

“…The recent advent of portable autonomous sound recorders has created a new way for ecological data to be acquired at scale, without many of the biases associated with human observers (Servick, 2014). Consequently, monitoring with passive acoustic recorders has garnered much attention and ecologists have been quick to embrace the technology (Burivalova et al, 2019; Oswald et al, 2022; Sugai et al, 2019). However, effective ecological monitoring using acoustic technology is more than just recording sound.…”