Invasive animal species are spreading rapidly across the globe, creating an urgent need for efficient early-detection and monitoring tools. Passive acoustic monitoring has become an established method...

YouTube video by Peter Oehlkers

Deep learning is transforming the study of animal sound, enabling the automated identification of species, individuals, behaviors, and ecological patterns from large collections of recordings. While bioacoustic machine-learning models are growing more powerful, many biologists—ecologists, behavioral scientists, conservationists—and others working with acoustic data feel unprepared to navigate the computational workflows required to implement them. This article presents practical guidelines covering the full lifecycle of bioacoustic machine learning, including problem definition, data sourcing and annotation, model training, evaluation, deployment, reproducibility, and ethical considerations. Rather than providing a linear checklist, the guidelines outline an iterative framework for building science-led workflows, leveraging transfer learning and open-source tools, evaluating models based on the real-world cost of errors, and addressing domain shift under variable field conditions. Ultimately, this workflow demystifies the software-engineering process, providing a low-barrier and reproducible pathway for researchers applying machine learning to animal sound.

Home page of the ENES Bioacoustics Research Lab.

Learning by imitation is the foundation for verbal and musical expression, but its underlying neural basis remains obscure. A juvenile male zebra finch imitates the multisyllabic song of an adult tutor in a process that depends on a song-specialized cortico-basal ganglia circuit, affording a powerful system to identify the synaptic substrates of imitative motor learning. Plasticity at a particular set of cortico-basal ganglia synapses is hypothesized to drive rapid learning-related changes in song before these changes are subsequently consolidated in downstream circuits. Nevertheless, this hypothesis is untested and the synaptic locus where learning initially occurs is unknown. By combining a computational framework to quantify song learning with synapse-specific optogenetic and chemogenetic manipulations within and directly downstream of the cortico-basal ganglia circuit, we identified the specific cortico-basal ganglia synapses that drive the acquisition and expression of rapid vocal changes during juvenile song learning and characterized the hours-long timescale over which these changes consolidate. Furthermore, transiently augmenting postsynaptic activity in the basal ganglia briefly accelerates learning rates and persistently alters song, demonstrating a direct link between basal ganglia activity and rapid learning. These results localize the specific cortico-basal ganglia synapses that enable a juvenile songbird to learn to sing and reveal the circuit logic and behavioral timescales of this imitative learning paradigm. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, K99 NS144525 (DCS), F32 MH132152 (DCS), F31 HD098772 (SB), R01 NS099288 (RM), RF1 NS118424 (RM and JP)

Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt, Zheng-Hua Tan

Autonomous recording units (ARUs) are an emerging technology that allows for passive monitoring of soniferous animals and soundscapes. Over the past decade, ARUs have become a popular tool for monitoring birds for their potential to reduce the labor and costs of traditional in-person sampling procedures. However, uncertainty surrounding factors affecting detection of avian taxa using ARUs can inhibit their monitoring efficacy. Eastern Black Rails (Laterallus jamaicensis jamaicensis) are a secretive marsh bird listed as a federally threatened species in the U.S.A. Eastern Black Rail vocalizations are difficult to detect by field personnel, and numerous in-person surveys can be required to confirm their presence at a site. While ARUs are an alternative for detecting Eastern Black Rails, it is unknown at what maximum distance an ARU can detect their vocalizations. We evaluated factors affecting the detection distance of simulated vocalizations for ARUs in four marsh vegetation types under a range of environmental conditions. Detection distances varied across models, vocalization and vegetation types, and call volume. Kickeedo vocalizations were detected at greater distances, and detection distances increased for all vocalization types in open vegetation. High relative humidity increased detection distances, while louder background noise decreased detection distances. High wind speeds in cordgrass (Spartina spp.) decreased detection probability disproportionately relative to other vegetation types. Based on these results, considerations of survey area, vegetation type, and site condition can allow land managers and researchers to optimize Eastern Black Rail monitoring using ARUs. Given the substantial staff time needed to monitor this species, ARUs may increase the likelihood of detection and provide an efficient alternative to in-person monitoring.

Over 20 species of geographically and phylogenetically diverse bird species produce convergent whining vocalizations towards their respective brood parasites. Model presentation and playback experimen...

Mammals play vital roles in ecological communities, but many are in rapid decline worldwide. Comprehensive monitoring of mammal populations is crucial for effective conservation, but large-scale m...