Passive acoustic monitoring of the rare orange-bellied parrot: automated detection using BirdNET-based transfer learning

Efficiently finding rare species is a perennial challenge in conservation science. The orange-bellied parrot Neophema chrysogaster is a rare mobile bird that is difficult to locate using traditional field survey techniques with human observers. We harnessed recent advances in bioacoustic technology to create a survey framework that integrates passive acoustic surveys and semi-automated detection to increase monitoring capacity for the orange-bellied parrot. We developed a custom BirdNET classifier for the orange-bellied parrot and compared efficacy of acoustic and field surveys using an occupancy framework. We deployed autonomous recording units across the orange-bellied parrots breeding range in southwest Tasmania and concurrently undertook between three and six repeated point-count surveys at the same 48 sites using human observers. Our custom BirdNET classifier had high accuracy and discrimination abilities. Validation of model scores across a week (5,712 hours of audio) required 60 hours reviewing time and yielded a 95% confidence of a correct BirdNET prediction at scores over 0.998. Occupancy analysis showed that the detection probability of acoustic surveys (p = 0.80) was more than eleven times greater than field surveys by skilled ecologists familiar with the species (p = 0.07). We provide a template for how to implement monitoring of the orange-bellied parrot and recommendations for how our methods can be improved to optimise the classifier to account for other species and locations.