Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.

Global pollinator decline urgently requires effective methods to assess their trends, distribution and behaviour. Passive acoustics is a non-invasive and cost-efficient monitoring tool increasingly employed for monitoring animal communities. However, insect sounds remain highly unexplored, hindering the application of this technique for pollinators. To overcome this shortfall and support future developments, we recorded and characterized wingbeat sounds of a variety of Iberian domestic and wild bees and tested their relationship with taxonomic, morphological, behavioural and environmental traits at inter- and intra-specific levels. Using directional microphones and machine learning, we shed light on the acoustic signature of bee wingbeat sounds and their potential to be used for species identification and monitoring. Our results revealed that frequency of wingbeat sounds is negatively related with body size and environmental temperature (between-species analysis), while it is positively related with experimentally induced stress conditions (within-individual analysis). We also found a characteristic acoustic signature in the European honeybee that supported automated classification of this bee from a pool of wild bees, paving the way for passive acoustic monitoring of pollinators. Overall, these findings confirm that insect sounds during flight activity can provide insights on individual and species traits, and hence suggest novel and promising applications for this endangered animal group. This article is part of the theme issue \'Towards a toolkit for global insect biodiversity monitoring\'.

An acoustic community is defined as an aggregation of species that produces sound by using internal or extra-body sound-producing tools. Such communities occur in aquatic (freshwater and marine) and terrestrial environments. An acoustic community is the biophonic component of a soundtope and is characterized by its acoustic signature, which results from the distribution of sonic information associated with signal amplitude and frequency. Distinct acoustic communities can be described according to habitat, the frequency range of the acoustic signals, and the time of day or the season. Near and far fields can be identified empirically, thus the acoustic community can be used as a proxy for biodiversity richness. The importance of ecoacoustic research is rapidly growing due to the increasing awareness of the intrusion of anthropogenic sounds (technophonies) into natural and human-modified ecosystems and the urgent need to adopt more efficient predictive tools to compensate for the effects of climate change. The concept of an acoustic community provides an operational scale for a non-intrusive biodiversity survey and analysis that can be carried out using new passive audio recording technology, coupled with methods of vast data processing and storage.

Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter).