Skip to main content
Dryad

From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify, and monitor insects

Kohlberg, Anna1; Myers, Christopher2; Figueroa, Laura1

Published Mar 04, 2024 on Dryad. https://doi.org/10.5061/dryad.hmgqnk9r1

Data files

Mar 04, 2024 version files 1.83 MB

Abstract

Insects play vital ecological roles; many provide essential ecosystem services while others are economically devastating pests and disease vectors. Concerns over insect population declines and expansion have generated a pressing need to effectively monitor insects across broad spatial and temporal scales. A promising new approach is bioacoustics, which uses sound to study ecological communities. Despite recent increases in machine learning technologies, the status of emerging automated bioacoustics methods for monitoring insects is not well known, limiting potential applications.

To address this gap, we systematically review the effectiveness of automated bioacoustics models over the past four decades, analyzing 176 studies that met our inclusion criteria. We describe their strengths and limitations compared to traditional methods and propose productive avenues forward.

We found automated bioacoustics models for 302 insect species distributed across nine Orders. Studies used intentional calls (e.g., grasshopper stridulation), by-products of flight (e.g., bee wingbeats), and indirectly produced sounds (e.g., grain movement) for identification. Pests were the most common study focus, driven largely by weevils and borers moving in dried food and wood. All disease vector studies focused on mosquitoes. A quarter of the studies compared multiple insect families.

Our review illustrates that machine learning, and deep learning in particular, are becoming the gold standard for bioacoustics automated modeling approaches. We identified models that could classify hundreds of insect species with over 90% accuracy. Bioacoustics models can be useful for reducing lethal sampling, monitoring phenological patterns within and across days, and working in locations or conditions where traditional methods are less effective (e.g., shady, shrubby, or remote areas). However, it is important to note that not all insect taxa emit easily detectable sounds, and that sound pollution may impede effective recordings in some environmental contexts.

Synthesis and applications: Automated bioacoustics methods can be a useful tool for monitoring insects and addressing pressing ecological and societal questions. Successful applications include assessing insect biodiversity, distribution, and behavior, as well as evaluating the effectiveness of restoration and pest control efforts. We recommend collaborations among ecologists and machine learning experts to increase model use by researchers and practitioners.